JNKVV RESEARCH JOURNAL Volume 45 Number 2 July - December 2011
Contents Research Paper Radiation use efficiency and physiological traits in Kalmegh (Andrographis paniculata Nees) 129 under various row spacings and nitrogen levels Sanjay Patidar, A.S. Gontia, Anubha Upadhyay, S. Rao and Preeti Sagar Nayak Population dynamics of surface herb flora in plantation forests of Jabalpur, Madhya Pradesh 136 P.K. Singhal and Ankur Shrivastava Population dynamics of parasitic nematodes in soybean grown under climatic conditions of 147 Kymore Plateau and Satpura Hills of Madhya Pradesh S.P. Tiwari and Jayant Bhatt Effect of botanicals, oil cakes and bio agents on root-knot (Meloidogyne incognita ) 150 management in chilli S.P. Tiwari, Jayant Bhatt, D.S. Dhakad and Madhuri Mishra Pathogenic potential of root knot nematode (Meloidogyne incognita ) on lentil 155 and its effects on plant age Jayant Bhatt, S.P. Tiwari and Kamlesh Pawar Tolerence ofTrichoderma viride to fungicides under artificial conditions 159 Ranavay Kumar, J.P. Upadhyay and Sanjeev Kumar Comparative efficacy of new and recommended insecticides against 162 soybean defoliators and stem borers Nanda Khandwe, Ansar Nadaf and Sandeep Sharma Correlation of yield and attributing characters in Dolichos bean under 168 conditions of Jabalpur, Madhya Pradesh D.P. Sharma, Niranjan Dehariya, Rajani Bisen and B.P. Bisen Effect of different temperatures and desiccants on quality of yellow button 172 type chrysanthemum flowers Beena Nair, Krishna Pal Singh and Vidhya Sankar M. Morphological study of a watershed using Remote Sensing and GIS approach 175 S.K. Sharma, N.K. Seth, S. Tignath and J.P. Shukla Geomorphometric study of Gusuru river watershed using Remote Sensing & GIS technique 181 S.K. Sharma, N.K. Seth and S. Tignath
Effect of ratio (n/v) on optimizing clearance angle and weeding efficiency 188 Devvrat Singh and Jayant Negi
Jawaharlal Nehru Krishi Vishwavidyalaya Jabalpur 482004 (Madhya Pradesh) India JNKVV RESEARCH JOURNAL ISSN : 0021-3721 Registration No.: 13-37-67
Agricultural resources mapping in GIS of Kymore plateau and Satpura hills 192 agro-climatic zone, Madhya Pradesh Varun Sharma and M.K. Hardaha Seed coverer- a tool to enhance field emergence and productivity of soybean 199 Devvrat Singh, A.K. Vyas, R. Ramteke, S.D. Billore and I.R. Khan Design parameters of border irrigation for clay loam soil at JNKVV main campus, Jabalpur 203 K.L. Mishra and M.K. Awasthi Planning and design of drainage system in Gopagwari village in the foot hills of Bargi dam 205 K.L. Mishra and M.K. Awasthi Development of artificial neural network based model for prediction of 210 stream flow in Wainganga catchment, Balaghat, Madhya Pradesh K.K. Madankar and M.K. Hardaha A multiple regression approach to predict production of paddy and wheat crops in 219 Madhya Pradesh K.S. Kushwaha and P.K. Awasthi Jack- knife technique and almost unbiased class of ratio type estimators in 223 circular systematic sampling scheme K.S. Kushwaha Analysis and estimation of soybean yield using ANCOVA technique 228 K.S. Kushwaha, K.K. Agrawal and Sanjay Jain Impact of integrated application of organic manure and chemical fertilizers on productivity 231 of soybean, wheat and chickpea grown on vertisols of Madhya Pradesh R.K. Bhatnagar, A.K. Dwivedi, B. Sachidanand and D.K. Pahalwan Efficient production and marketing of soybean in Central India: An initiative of ITC e-Soya Choupal 235 H.O. Sharma and S.B. Nahatkar Web based information system of aromatic plants using IT tools for Agri-rural development 240 A.K. Rai, Bharati Dass, Ratnesh Sahu and Akhilesh Raghuwanshi Accessing electronic resources at University Central Library in the digital eon: an appraisal 244 Siddarth Nayak, Vinita Pandey, Sharad Kumar Jain and Preeti Sagar Nayak Authorship distinctiveness of research papers dealing with agriculture and allied subjects 253 published in JNKVV Research Journal Siddarth Nayak, Vinita Pandey, Preeti Sagar Nayak and Sharad Kumar Jain
Published by : Dr. S.S. Tomar, Dean, Faculty of Agriculture, JNKVV, Jabalpur 482 004 (M.P.),India Printed at : M/s Fortune Graphics & Scanning Centre, Golebazar, Jabalpur 482 002 (M.P.) STATEMENT OF OWNERSHIP FORM IV (See Rule 8)
Place of Publication : Jabalpur (Madhya Pradesh), India
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Publisher's Name : Dr. S.S. Tomar Indian Dean, Faculty of Agriculture JNKVV, Jabalpur 482 004 (M.P.), India
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Editor's Name : Dr. Mohan S. Bhale Indian Senior Scientist Department of Plant Pathology JNKVV, Jabalpur 482 004 (M.P.), India
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General Information: JNKVV Research Journal is the publication of J.N. Agricultural University (JNKVV), Jabalpur for records of original research in basic and applied fields of Agriculture, Agricultural Engineering, Veterinary Science and Animal Husbandry. It is published twice a year. The journal is abstracted in CAB International abstracting system, Biological Abstracts, Indian Science Abstracts. Membership is open to all individuals and organizations coping with the mission of the University and interested in enhancing productivity, profitability and sustainability of agricultural production systems and quality of rural life through education, research and extension activities in the field of agriculture and allied sciences.
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ISSN : 0021-3721 Registration No. : 13-37-67
Published by : Dr. S.S. Tomar, Dean, Faculty of Agriculture, JNKVV, Jabalpur 482 004 (M.P.), India Printed at : M/s Fortune Graphics & Scanning Centre, Sahu Mohalla, Golebazar, Jabalpur (M.P.) ISSN : 0021-3721 JNKVV Volume 45 Research Journal Number (2) 2011
Contents
Research Paper Radiation use efficiency and physiological traits in Kalmegh (Andrographis paniculata Nees) 129 under various row spacings and nitrogen levels Sanjay Patidar, A.S. Gontia, Anubha Upadhyay, S. Rao and Preeti Sagar Nayak
Population dynamics of surface herb flora in plantation forests of Jabalpur, Madhya Pradesh 136 P.K. Singhal and Ankur Shrivastava
Population dynamics of parasitic nematodes in soybean grown under climatic conditions of 147 Kymore Plateau and Satpura Hills of Madhya Pradesh S.P. Tiwari and Jayant Bhatt
Effect of botanicals, oil cakes and bio agents on root-knot (Meloidogyne incognita) management 150 in chilli S.P. Tiwari, Jayant Bhatt, D.S. Dhakad and Madhuri Mishra
Pathogenic potential of root knot nematode (Meloidogyne incognita) on lentil and its effects on 155 plant age Jayant Bhatt, S.P. Tiwari and Kamlesh Pawar
Tolerence of Trichoderma viride to fungicides under artificial conditions 159 Ranavay Kumar, J.P. Upadhyay and Sanjeev kumar
Comparative efficacy of new and recommended insecticides against soybean defoliators and 162 stem borers Nanda Khandwe, Ansar Nadaf and Sandeep Sharma
Correlation of yield and attributing characters in Dolichos bean under conditions of Jabalpur, 168 Madhya Pradesh D.P. Sharma, Niranjan Dehariya, Rajani Bisen and B.P. Bisen
Effect of different temperatures and desiccants on quality of yellow button type chrysanthemum 172 flowers Beena Nair, Krishna Pal Singh and Vidhya Sankar M.
Morphological study of a watershed using Remote Sensing and GIS approach 175 S.K. Sharma, N.K. Seth, S. Tignath and J.P. Shukla
Geomorphometric study of Gusuru river watershed using Remote Sensing & GIS technique 181 S.K. Sharma, N.K. Seth and S. Tignath
Effect of ratio (n/v) on optimizing clearance angle and weeding efficiency 188 Devvrat Singh and Jayant Negi Agricultural resources mapping in GIS of Kymore plateau and Satpura hills agro-climatic zone, 192 Madhya Pradesh Varun Sharma and M.K. Hardaha
Seed coverer- a tool to enhance field emergence and productivity of soybean 199 Devvrat Singh, A.K. Vyas, R. Ramteke, S.D. Billore and I.R. Khan
Design parameters of border irrigation for clay loam soil at JNKVV main campus, Jabalpur 203 K.L. Mishra and M.K. Awasthi
Planning and design of drainage system in Gopagwari village in the foot hills of Bargi dam 205 K.L. Mishra and M.K. Awasthi
Development of artificial neural network based model for prediction of stream flow in Wainganga 210 catchment, Balaghat, Madhya Pradesh K.K. Madankar and M.K. Hardaha
A multiple regression approach to predict production of paddy and wheat crops in 219 Madhya Pradesh K.S. Kushwaha and P.K. Awasthi
Jack- knife technique and almost unbiased class of ratio type estimators in circular systematic 223 sampling scheme K.S. Kushwaha
Analysis and estimation of soybean yield using ANCOVA technique 228 K.S. Kushwaha, K.K. Agrawal and Sanjay Jain
Impact of integrated application of organic manure and chemical fertilizers on productivity 231 of soybean, wheat and chickpea grown on vertisols of Madhya Pradesh R.K. Bhatnagar, A.K. Dwivedi, B. Sachidanand and D.K. Pahalwan
Efficient production and marketing of soybean in Central India: An initiative of ITC e-Soya Choupal 235 H.O. Sharma and S.B. Nahatkar
Web based information system of aromatic plants using IT tools for Agri-Rural development 240 A.K. Rai, Bharati Dass, Ratnesh Sahu and Akhilesh Raghuwanshi
Accessing electronic resources at University Central Library in the digital eon: an appraisal 244 Siddarth Nayak, Vinita Pandey, Sharad Kumar Jain and Preeti Sagar Nayak
Authorship distinctiveness of research papers dealing with agriculture and allied subjects 253 published in JNKVV Research Journal Siddarth Nayak, Vinita Pandey, Preeti Sagar Nayak and Sharad Kumar Jain
Issued : 31st December, 2011 JNKVV Res J 45(2): 129-135 (2011)
Radiation use efficiency and physiological traits in Kalmegh (Andrographis paniculata Nees) under various row spacings and nitrogen levels
Sanjay Patidar, A.S. Gontia, Anubha Upadhyay, S. Rao and Preeti Sagar Nayak Department of Plant Physiology College of Agriculture Jawaharlal Nehru Krishi Vishwa Vidyalaya Jabalpur 482 004 (MP)
content is also reported to be affected by changes in Abstract light intensity (Treshow 1970; Kapur 1999).
The investigations were carried out at the Crop Farm of Dusty The productivity of a crop depends on its capacity Acre Area, under Department of Plant Physiology, JNKVV, for photosynthesis, its photosynthetic area and the Jabalpur during kharif 2008-09 to study the influence of three utilization of photosynthetically active radiation within crop row-spacings viz; 15, 30 & 45 cms and five Nitrogen levels canopy. The genetic variability of yield differences among viz; 0, 40, 60, 80 and 100 kg/ha on radiation use efficiency, the cultivars may be related to some of the growth physiological traits and productivity in kalmegh (Andrographis parameters viz., crop growth rate, relative growth rate, Nees). 30 cm row-spacing, 80 kgN/ha and their paniculata leaf area index, leaf area duration and specific leaf combinations exhibited maximum seed and herbage yields owing to higher dry matter production, leaf area index, leaf weight. The relationship between radiation use efficiency, area duration, crop growth rate, relative growth rate, specific photosynthetic efficiency and crop yield is a complex phenomenon. The yield is an integrated effect of leaf area, net photosynthesis, stomatal conductance, CO2 and
H2O utilization, transpiration rate, radiation use efficiency and numerous physiological processes and morphological chlorophyll index. yield components. Therefore, the productivity of cultivars depends in achieving better adoption to many Keywords: Kalmegh, radiation use efficiency, row- environmental factors including the hazards of diseases, spacings lodging and rains. For our understanding of the differences in yield in various cultivars of medicinal plants a physiological approach is needed. An understanding Kalmegh (Andrographis paniculata Nees) is a genus of of the physiological processes involved in the economic herbs and shrubs, distributed mostly in the tropical and production in medicinal plants such as vegetative growth, moist regions. It comprises of about 19 plant species formation of storage organs and seed filling, helps in found in India and Sri Lanka, certain parts of Thailand determining the best combination of the above three and Bangladesh. In India it is grown in Assam, Bihar, requirements and also suggests as to what Karnataka, Karla, Madhya Pradesh, Andhra Pradesh and improvements can be made to achieve a further increase West Bengal. Kalmegh, also known as “King of bitter” is in economic productivity in medicinal plants under given one among the prioritized medicinal plants in India, and set of conditions. Keeping in view of the above facts the this herb is being used mainly for treating fever, liver present investigations are undertaken. disease, diabetes, snake bite etc. The leaf and the whole herb contain the medicinal properties and is useful in treatment of diabetes, influenza, bronchitis, Materials and methods hepatomegaly, skin disorder and many such diseases (Patra et al. 2004). In addition to the alkaloid, the plant is The experiment was carried out at the Research Farm, also rich in other metabolites such as, total protein, amino Dusty Acre area, Department of Crop Physiology, acid and carbohydrates which act as the primary JNKVV, Jabalpur (M.P.) during the kharif season of constituents for the growth of any plant which are greatly 2008-09. The experiment was laid out in a Split plot affected by changes in light intensity. The total chlorophyll design with three replications. Three row spacings viz;
129 15 cm (S1), 30 cm (S2) and 45 cm (S3) were taken as requirements for attainting higher LAI and LAD in main treatments, while five nitrogen levels viz; 0 (N0) , combination which might have resulted in availability of
40 kg/ha (N1) , 60 kg/ha (N2) , 80 kg/ha (N3) and 100 kg/ all growth factors in abundance resulted in higher ha (N4) as subtreatments. Seeds of kalmegh were sown magnitudes of these parameters. The LAI had positive in the field adopting recommended cultural practices. association with grain yield Sahoo and Guru (1998) and The date of sowing and harvesting were 17th July 2008 was found to be increased with increase in daily radiation, and 3rd January 2009, respectively. plant canopy closed sooner, leaf area duration (LAD) increased and plant dry matter weight, leaf area index The leaf area index (LAI) was determined as per (LAI) and crop growth rate (CGR) were higher up to the specifications of Gardner et al. (1985), whereas, Leaf middle of seed development as row distance decreased area duration (LAD), Crop growth rate (CGR), Relative Pourhadian and Khajehpour (2008). growth rate (RGR), Specific leaf area (SLA) were determined as per Watsons (1952) method. Radiation Results showed in respect of RGR, CGR and SLA use efficiency was determined as per specifications of that among main treatments S2 (0.0168 and 0.564) had Sinclair and Muchow (1999). Physiological traits viz; the maximum RGR and CGR at 60-80 DAT and stomatal conductance, transpiration rate, net minimum was recorded in S1 (0.0146 and 0.505) (Table photosynthesis, CO2 and H2O utilization were quantified 1). Among sub treatments N3 (0.0168 and 0.562) by using infra-red gas analyser (IGRA, model CI 340) recorded the significant more over the rest of the sub whereas chlorophyll index was recorded by using treatments and N0 (0.0132 and 0.515) registered chlorophyll meter (Model CCM 200). significant minimum value. In interactions S2N3 (0.0207 and 0.653) had the significant highest magnitudes over the remaining interactions and treatment combinations Results and Discussion S1N0 (0.0120 and 0.390) were associated with the lowest.
Results also revealed that among main treatments S2
The investigations revealed significant differences (1.036) possessed the significant highest SLA and S1 among main treatments, sub treatments and interactions (0.992) minimum at 60-80 DAT. Among sub treatments for the most of the traits under study (Table 1-4). The N3 (1.023) noted the significant highest and N0 (1.002) results revealed that at 60- 80 DAT (days after was associated with minimum value. In interactions S2N3 transplanting) main treatment S2 (1.20 and 23.67) (1.054) had the significant more and S1N0 (0.978) was possessed the significant maximum leaf area index and associated with the lowest magnitude. SLA possessed leaf area duration and the minimum was observed in S1 positive association with grain yield Sahoo and Guru (1.12 and 21.95), respectively. Among sub treatments (1998). N (1.23 and 24.17) showed the significant highest values 3 Row spacing of 30 cm (S ) (0.0323 and 11.79) and N (1.07 and 20.88) the minimum. In interactions 2 0 was associated with the significant maximum radiation S2N3 (1.27 and 25.10) showed the significant highest values and treatment combination S N (1.01 and 20.03) use efficiency (RUE) and chlorophyll index over rest of 1 0 the treatments and S (15cm) minimum (0.0311 and recorded the lowest magnitude for these traits. In most 1 5.28). In sub treatments N (0.0320) and N (13.27) of the crops a higher LAI was desired for higher 4 3 showed the significant more values and N (0.0300) and productivity, further increase in LAI beyond optimum 1 N (2.96) significant minimum (Table 1). In interactions caused lodging and reduction in yield Nichipororich 0 S N (0.0344 and 19.80) had the significant maximum (1970). However optimum LAI reached well before 2 3 values and treatment combinations S N (0.0291) and anthesis and fell progressively as water stress increased 2 1 S N (1.78) minimum. Tei . (2004) workedout the Fisher and Kohn (1998). Leaf area index and interception 2 0 et al of photosynthetically active radiation changed with row critical % N dilution curve to analyse the effect of N spacings but depended on the phenological stages, plant availability on light interception and radiation use density, canopy architecture and management system efficiency, confounded that in all years, the critical Strieder et al. (2008). The study also revealed significant “uptake” curve was very close to the uptake curves variation among interactions for LAI. It has been recorded obtained with the fertilizer N rate of 100 kg N/ha. in 1996, that treatment combinations of 30 cm row-spacing with 200 kg/ha. in 1997 and 150kg/ha. in 1999 due to the 80 kgN/ha were found to be superior for optimization of increases of water content related to the osmotic effect LAI and LAD. Further increase in either row spacings or of nitrates. Increasing N supply increased light N levels appears to be not desired for increasing these interception throughout the growth cycle and slightly traits. The higher LAI and LAD in treatment combinations increased radiation use efficiency. Water use efficiency (WUEDM) is directly related to radiation use efficiency S2N3 and S2N4 was attributed to fulfilment of the optimum
130 Table 1. Morpho-physiological parameters as influenced by various treatments
Treatments LAI LAD RGR CGR SLA RUE Chlorophyll (m2.days) (g/g/day) (g/m2/day) (m2/g) (g/µmol/m2/s) index (g/ m2) Main Treatments (Row –spacings)
S1 (15 cm) 1.12 21.95 0.0146 0.505 0.9920 0.0301 5.38
S2 (30 cm) 1.20 23.67 0.0168 0.564 1.0360 0.0323 11.79
S3 (45 cm) 1.19 23.29 0.0162 0.551 1.0080 0.0311 9.80 SEm ± 0.01 0.02 0.00006 0.0020 0.01100 0.00017 0.61 C.D. 5% 0.02 0.06 0.00023 0.0090 0.04100 0.00065 2.41 Sub Treatments (N levels)
N0 (Control) 1.07 20.88 0.0132 0.515 1.0020 0.0309 2.96
N1 (40 kg/ha) 1.17 22.78 0.0145 0.520 1.0040 0.0300 7.07
N2 (60 kg/ha) 1.17 23.08 0.0164 0.550 1.0140 0.0316 9.01
N3 (80 kg/ha) 1.23 24.17 0.0178 0.562 1.0230 0.0314 13.27
N4 (100 kg/ha) 1.21 23.96 0.0174 0.551 1.0180 0.0320 12.66 SEm ± 0.01 0.03 0.00009 0.0020 0.01700 0.00049 0.54 C.D. 5% 0.02 0.10 0.00026 0.0700 0.05000 0.00143 1.56 Interactions (Row – spacings × N levels)
S1N0 1.01 20.03 0.0120 0.390 0.9780 0.0299 2.80
S1N1 1.14 22.23 0.0149 0.477 0.9960 0.0306 5.13
S1N2 1.12 21.80 0.0150 0.484 1.0000 0.0306 4.52
S1N3 1.17 23.03 0.0162 0.545 1.0080 0.0292 6.76
S1N4 1.15 22.67 0.0160 0.531 1.0070 0.0302 7.71
S2N0 1.10 21.50 0.0139 0.477 0.0940 0.0333 1.78
S2N1 1.20 23.50 0.0162 0.561 1.0100 0.0291 10.53
S2N2 1.18 23.47 0.0164 0.564 1.0290 0.0314 8.40
S2N3 1.27 25.10 0.0207 0.653 1.0540 0.0344 19.80
S2N4 1.26 24.80 0.0198 0.620 1.0450 0.0333 18.45
S3N0 1.09 21.10 0.0121 0.468 0.9620 0.0294 4.30
S3N1 1.17 22.63 0.0152 0.516 1.0040 0.0304 5.56
S3N2 1.21 23.97 0.0171 0.589 1.0290 0.0329 14.10
S3N3 1.24 24.37 0.0171 0.608 1.0360 0.0306 13.24
S3N4 1.23 24.40 0.0171 0.612 1.0420 0.0325 11.81 SEm ± 0.01 0.06 0.00034 0.0040 0.02900 0.00085 0.93 C.D. 5% 0.03 0.17 0.00100 0.0130 0.08400 0.00247 2.75 and inversely related to crop conductance. They had reflected in its highest CO2 and H2O utilization which proposed that reduced WUEDM, shortage of nitrogen are the key factors in influencing the photosynthetic results from a reduction in radiation use efficiency productivity of the whole crop stand. Higher stomatal proportionally greater than the fall in conductance conductance and transpiration rates were also noted in Caviglia and Sadras (2001). If all leaves have the same the same row- spacing, traits beneficial in terms of CO2 daily photosynthetic radiation use efficiency, than the and PAR uptake as long as the water availability is in whole canopy possessed the same Haxeltine and abundance. The genotypes produced higher seed yield Prentice (1996). The results showed the influence of also showed higher value of net photosynthesis and nitrogen nutrient in increasing chlorophyll content in stomatal conductance Kalpana et al. (2003). The rate of leaves as the nitrogen is involved in chloroplast photosynthesis was positively correlated with stomatal development and is essential component of chlorophyll conductance, transpiration rate, leaf temperature and molecule Pirjal et al. (1971), Khalil et al. (1992), Panwar grain yield Singh et al. (1997). Increased CO 2 and Singh (2000). concentration of air to 400 ppm reduced the transpiration Row-spacing of 30cm possessed the higher rate of corn by causing the stomata to close completely photosyntically active radiation (PAR) interception which Pollas et al. (1975).
131 Table 2. Physiological parameters as influenced by various treatments
Treatment PAR Stomatal Transpiration Net CO2 utilization H2O utilization Conductance rate Photosynthesis (µmol/m2/s) (mmol/m2/s) (mmol/m2/s) (µmol/m2/s) (ppm) (KPa)
Main Treatments (Row –spacings)
S1 (15 cm) 1011.53 108.93 2.67 18.12 65.73 0.356
S2 (30 cm) 1167.53 116.93 3.39 21.55 74.00 0.420
S3 (45 cm) 1156.27 116.40 3.16 20.68 72.27 0.397 SEm ± 17.00 0.29 0.01 0.33 0.42 0.005 C.D. 5% 66.75 1.14 0.04 1.31 1.66 0.018 Sub Treatments (N levels)
N0 (Control) 885.22 106.33 1.93 16.78 61.33 0.295
N1 (40 kg/ha) 1074.33 110.89 2.79 19.30 68.33 0.379
N2 (60 kg/ha) 1128.89 115.22 3.39 20.19 72.11 0.408
N3 (80 kg/ha) 1250.56 118.22 3.55 22.58 75.22 0.438
N4 (100 kg/ha) 1219.89 119.78 3.70 21.76 76.33 0.434 SEm ± 22.26 0.50 0.02 0.29 0.61 0.004 C.D. 5% 64.97 1.45 0.05 0.86 1.77 0.013 Interactions (Row – spacings × N levels)
S1N0 888.33 106.33 1.98 15.70 58.00 0.291
S1N1 970.00 102.33 2.35 18.00 63.67 0.352
S1N2 996.33 109.67 2.87 18.17 66.33 0.364
S1N3 1118.00 111.67 2.97 19.60 69.67 0.394
S1N4 1085.00 114.67 3.17 19.16 71.00 0.378
S2N0 824.00 104.00 2.15 17.00 64.67 0.310
S2N1 1197.33 117.33 3.36 21.23 73.33 0.415
S2N2 1159.00 116.00 3.59 20.53 72.33 0.421
S2N3 1338.67 124.67 3.89 25.00 80.33 0.487
S2N4 1318.67 122.67 3.94 24.00 79.33 0.466
S3N0 943.33 108.67 1.66 17.63 61.33 0.283
S3N1 1055.67 113.00 2.65 18.66 68.00 0.370
S3N2 1231.33 120.00 3.70 21.87 77.67 0.440
S3N3 1295 118.33 3.79 23.14 75.67 0.433
S3N4 1256.00 122.00 3.98 22.13 78.67 0.458 SEm ± 38.55 0.86 0.03 0.51 1.05 0.01 C.D. 5% 112.53 2.50 0.08 1.49 3.06 0.02
The stomatal conductance maximum is a stomata when the sun is bright Chen et al. (1999). numerical measure of the maximum rate of passage of However, the speed of photosynthesis depends on being either water vapour or CO through the stomata. It plays 2 able to release the CO2 produced into the atmosphere, an important role in the plant atmosphere water so closing the stomata too much as far too long reduces exchange and hence is a key parameter in many photosynthesis, so plants keep opening and closing of ecological models. Diffusion of CO2 into leaves and water stomata to keep a middle line between the two vapour from the leaves to the atmosphere is mainly constraints. drives by the stomatal aperature, which is controlled by a complex system of plant physiological processes. If Among subtreatments N level 80 kg/ha (N3) was speed of conductance is too great plant transpires a lot found to be associated with the higher magnitudes of of water and soil dries out placing the plant in water these physiological traits which may be attributed to the stress. To avoid the condition plants try to some extent development of efficient canopy architecture for to control the speed of transpiration by closing the exploitation of growth factors resulting in higher uptake
132 Table 3. Yield Components and Yields as influenced by various treatments Treatments Plant height No. of No. of No. of 1000 seed (cm) Branches/plant pods/plant seeds/pod weight (g)
Main Treatments (Row –spacings)
S1 (15 cm) 39.25 18.33 248.47 10.83 1.53
S2 (30 cm) 47.91 22.57 272.20 12.67 1.66
S3 (45 cm) 45.59 20.91 263.53 12.07 1.63 SEm ± 0.57 0.07 0.76 NS 0.01 C.D. 5% 2.25 0.26 2.98 NS 0.04 Sub Treatments (N levels)
N0 (Control) 34.66 16.76 228.78 9.39 1.44
N1 (40 kg/ha) 42.67 19.53 256.78 11.67 1.57
N2 (60 kg/ha) 45.08 20.26 264.11 12.22 1.63
N3 (80 kg/ha) 49.89 23.31 279.44 13.06 1.69
N4 (100 kg/ha) 48.96 23.16 277.89 12.94 1.71 SEm ± 0.56 0.27 1.19 0.43 0.01 C.D. 5% 1.65 0.80 3.47 1.24 0.04 Interactions (Row – spacings × N levels)
S1N0 32.37 16.07 223.33 8.00 1.41
S1N1 39.80 18.60 248.67 11.33 1.51
S1N2 38.32 17.73 244.33 11.00 1.54
S1N3 44.07 20.10 265.67 12.00 1.61
S1N4 41.70 19.13 260.33 11.83 1.60
S2N0 36.81 17.40 234.33 10.83 1.49
S2N1 46.18 20.40 269.00 12.17 1.62
S2N2 47.94 21.03 275.33 12.67 1.64
S2N3 55.10 27.67 293.33 14.00 1.75
S2N4 53.53 26.33 289.00 13.67 1.78
S3N0 34.81 16.80 228.67 9.33 1.43
S3N1 42.05 19.60 252.67 11.50 1.58
S3N2 48.98 22.00 272.67 13.00 1.70
S3N3 50.49 22.17 279.33 13.17 1.71
S3N4 51.64 24.00 284.33 13.33 1.74 SEm ± 0.98 0.47 2.06 NS 0.02 C.D. 5% 2.85 1.38 6.00 NS NS of PAR which in turn might have reflected in the higher treatment may be attributed to the optimum crop stand net photosynthesis. Treatment combinations of 30 cm geometry and canopy architature for exploitation of row-spacing with 80 kg N/ha (S2N3) was found to be growth factors. associated with the higher performance of PAR uptake, stomatal conductance, transpiration rate, net 80 kg/ha N level was found to be associated with maximum herbage as well as seed yields due to higher photosynthesis, CO2 and H2O utilization. 25 % light intensity was found to reduce the transpiration rate by magnitudes of yield components. Higher N levels were 50% Bhatt et al. (2006). found to increase plant height, pod number (Butter and Aulakh 1999), branch number, herbage and dry leaf Row-spacing of 30cm possessed the highest yields (Subramanian, 2008), (Balyan et al. 1987). magnitudes of plant height, no. of branches/plant, no. of Treatment combinations S2N3 (30cm row-spacing + 80 pods/plant, no. of seeds/pod, 1000 seed weight and HI kg N/ha) recorded the maximum seed and herbage
(Table 3-4) which in turn had reflected in highest herbage productivity, whereas S1N0 (15 cm row-spacing + 0 kg and seed productivity. The higher productivity in this N/ha) the minimum.
133 Table 4. Herbage Yield and seed yield as influenced by various treatments Treatments Herbage yield Herbage yield Seed Yield Seed Yield Harvest Index (g/plant) (kg/ha) (g/plant) (kg/ha)
Main Treatments (Row –spacings)
S1 (15 cm) 17.51 4289.68 2.53 959.40 11.06
S2 (30 cm) 25.43 5876.20 3.15 1088.20 14.34
S3 (45 cm) 23.63 5552.32 2.76 1045.67 13.23 SEm ± 0.65 60.95 0.02 4.63 0.32 C.D. 5% 2.57 239.29 0.07 1.18 1.27 Sub Treatments (N levels)
N0 (Control) 14.11 3322.17 2.14 826.00 9.72
N1 (40 kg/ha) 19.98 5020.33 2.71 1013.78 12.10
N2 (60 kg/ha) 22.68 5405.34 2.75 1055.11 12.80
N3 (80 kg/ha) 28.02 6475.66 3.27 1135.56 15.40
N4 (100 kg/ha) 26.17 5973.50 3.19 1125.00 14.34 SEm ± 0.64 70.33 0.03 1.62 0.28 C.D. 5% 1.86 205.28 0.08 4.74 0.82 Interactions (Row – spacings × N levels)
S1N0 12.77 3098.77 1.99 778.67 9.13
S1N1 17.94 4273.83 2.62 955.67 11.27
S1N2 16.83 3922.55 2.49 923.00 10.90
S1N3 21.02 5561.10 2.78 1088.67 12.45
S1N4 19.00 4592.17 2.75 1051.00 11.55
S2N0 15.26 3608.25 2.15 871.33 10.27
S2N1 22.62 5717.83 2.81 1099.00 12.74
S2N2 24.89 6008.53 2.84 1118.33 13.38
S2N3 33.23 7198.23 4.06 1181.33 18.95
S2N4 31.17 6848.13 3.88 1171.00 16.34
S3N0 14.30 3259.49 2.28 828.00 9.77
S3N1 19.37 5069.34 2.68 986.67 12.30
S3N2 26.33 6284.93 2.92 1124.00 14.13
S3N3 29.81 6667.63 2.96 1136.67 14.81
S3N4 28.35 6480.20 2.93 1153.00 15.14 SEm ± 1.11 121.81 0.04 2.81 0.49 C.D. 5% 3.23 355.56 0.13 8.21 1.42
Conclusions izLrqr vUos"k.k tokgjyky usg: —f"k fo’ofo|ky; ds Qly dk;Zdh vuqla/kku {ks= esa rhu drkj varjks Øek'k% 15] 30 ,oa 45 ls-eh- Thus the investigations revealed that 30 cm row-spacing, 0 40 60 80 100 80 kg nitrogen/ha and their interactions were associated ,oa ikWp u=tu Lrjksa Øe’k% ] ] ] ,oa fd-xzk-gs- with maximum seed and herbage yields owing to the dk ÅtkZ mi;ksx {kerk] dk;Zdh; iSekus ,oa mRikndrk ij izHkko higher magnitudes of leaf dry matter, total DM production, tkuus gsrq [kjhQ 2008&09 esa fd;s x;sA 30 ls-eh- drkj varj] LAI, LAD, CGR, RGR, SLA, PAR absorption, net 80 fd-xzk-@gs- u=tu Lrj ,oa mudk feJ.k mRikndrk esa lokZsRre photosynthesis, stomatal conductance, transpiration rate, chlorophyll index, radiation use efficiency, CO and Fkk] ftldk dkj.k vf/kd ikS/k 'kq"d Hkkj iRrh {ks=Qy lwpd] iRrh 2 {ks=Qy vof/k] Qly o`f) nj] vkisf{kd o`f) nj] fof’k"B iRrh H2O utilization reflected in maximum no. of branches, no. of pods/plant, no. of seeds/pod, 1000 seed weight, {ks=Qy] 'kq/n ykHk izdk’k&laLys"k.k] okrja?kz lapkyu] dkcZu MkbZvkDlkbM] HI and subsequently economic productivity. ,oa ikuh mi;ksx] mLonsu nj] ÅtkZ mi;ksx {kerk ,oa DsyksjksfQy
134 lwpd ik;k x;k A translocate in smooth muscle. Am J Physiol 263 (3) 714-719 Nichipororich AA (1970) Great achievement of biological References sciences in raising the production of plant. Soviet J Ecol 1: 1-5 Panwar JDS, Singh O (2000) Response of Azospirillum and Balyan SS, Pal S, Sharma NS, Singh SN, Sobti SN (1987) Bacillus on growth and yield of wheat under field Effect of spacing nitrogen and phosphorus on the conditions. Indian Journal of Plant Physiology 5:108– growth and yield of two Ocimum species. Indian 110 Performer 31 (2): 89-96 Patra DD, Chattopadhyay A, Singh AK, Tomar VKS, Singh A, Bhatt RK, Tiwari HS, Mishra PL (2006) Photosynthesis and Mishra HO, Alam M, Khanuja SPS (2004) Agro shade tolerance in tropical range grasses and technology of kalmegh ( ). J legumes. Indian Journal of plant physiology 11(2): Andrographis paniculata Med and Aromatic Plant Sci 26: 475-478 172-177 Pirjal L, Milica CI Pica I (1971) Investigations on the effect of Buttar GS, Aulakh CS (1999) Effect of sowing date, nitrogen nutrient regime on some morphological and and row spacing on growth, yield attributes and yield biochemical indices in capitole wheat under irrigated of Indian mustard (Brassica juncea) Indian J Agron condition. Field crop Abstr 26(1) 44(4): 813-815 Pollas JE (1975) Transpiration and stomatal opening with Caviglia OP, Sadras VO (2001) Effect of nitrogen supply on changes in carbon dioxide content of the air. Crop crop conductance water and radiation use efficiency Sci 15: 793-797 of wheat. INTAEEA, Parana, Argentina. Pourhadian H, Khajehpour MR (2008) Effect of row spacing Chen JJ, Sun YW, Sheen TF (1999) Use of cold water and planting density on growth indices and yield of irrigation reduces stem elongation of plug-grown safflower, local variety of Isfahan “ koseh” in summer tomato and cabbage seedling. Hort Science 34, 852- planting. Journal of Science and Technology of 854 Agriculture and Natural Resourse 11 (42): 17-32 Fisher RA, Kohn GD (1996) The relation of grain to vegetative Sahoo C, Guru (1998) Physiological basis of yield variation in growth and post flowering leaf area in the wheat crop short duration on cultivars of rice. Indian J Plant under condition of limited soil moisture. Aust J Agril Physiol 3(1):36-41 Res 17: 281-295 Sinclair TR, Muchow RC (1999) Radiation use efficiency. Gardner FD, Pearce RB, Mitchell RL (1985) Physiology of Advance in Agronomy 65: 215-265 Crop Plants. Iowa State University Press Singh AK, Singh HP, Singh A, Gupta MM (1997) Domestication Haxeltine A, Prentice IC (1996) A general model for the light and evaluation of Kalmegh populations. CIMAP, use efficiency of primary production. Functional Lucknow, India Ecology 10: 551-561 Strieder ML, Silva PRF, Rambo L, Bergamaschi H, Dalmago Kalpana M, Chettia MB, Ratnam BP (2003) Phenological GA (2008) Canopy traits and corn yield under changer in photosynthetic rate, transpirations and different row-spacings and management system. stomatal conductance and their relationship with Pesquisa Agropecuaria Brasileira 43 (3): 309-317 seed yield in cowpea. Indian Journal of plant physiology 8 (2): 160-164 Subramanian R, Asmawi MZ, Sadikun A (2008) In vitro alpha- glucosidase and alpha-amylase enzyme inhibitory Kapur P (1999) Chlorophyll as an indicator of light intensity in effects of Andrographis paniculata extract and Andrographis paniculata. Indian J Plant Physiol 4:15- andrographolide. Acta Biochim Pol 55(2):391-398 19 Tei F, Benincasa P, Guiducci, M (2004) Critical nitrogen Khalil RA, Lajoie C, Resnick MS, Morgan KG (1992) Ca (2+)- (Manuscriptconcentration Received: 05.06.2011; in lettuce. Accepted: Plant Physiol 15.07.2011) 134 (1): 388- independent isoform of protein kinase C differentially 400 Treshow M (1970) Environment and plant Response. McGraw- Hill Book Company. N.Y Watson DJ (1952) The physiological basis for variation in yield. Adv Agron 4: 101-145
135 JNKVV Res J 45(2): 136-146 (2011)
Population dynamics of surface herb flora in plantation forests of Jabalpur, Madhya Pradesh
P.K. Singhal and Ankur Shrivastava Department of Bioscience Rani Durgawati University Jabalpur 482001 (MP)
Abstract Key words: Surface herb flora, biodiversity, indices of diversity, density, tropical deciduous forest plantations
Population dynamics of surface herb flora in 25 year old plantation forests of Jabalpur, Madhya Pradesh, viz. Acacia Biological diversity means the variability among living auriculiformis, Tectona grandis, Hardwickia binata, Eucalyptus organisms from all sources including inter alia, terrestrial, camadulensis, Albizia porcera and Terminalia arjuna, have marine and other aquatic ecosystems and the ecological been measured in the present study. The plantations recorded complexes of which they are a part. This includes the occurrence of total 281 species belonging to 171 genera diversity within species, between species and of and 58 families of surface herb flora. Among different ecosystems (CBD 1992). Biodiversity refers to the plantations, T. arjuna recorded the maximum species richness (178 species, 119 genera and 44 families), and H. binata variability within the ecological complex where it exists recorded the minimum species richness (155 species, 108 and that is essential for proper functioning of the genera and 34 families). The herbaceous ground flora of all ecosystem (CBD 1992). It is a vital provider of goods the plantations witnessed a high turnover rate of species and services to the sustenance of the present and future according to the seasons, with the maximum during rainy and human civilizations. However, today human themselves the minimum during summer seasons. Shannon-Weiner index are posing the greatest threat to the quality and quantity of diversity showed much variation with respect to different of biodiversity, resulting in its wide spread losses and plantations varying from 2.72 to 3.64. The maximum index of aberration in ecosystem functions and stability (CBD diversity (3.64) was noted in A. procera plantation, followed 1992). by that in A. auriculiformis (3.59), E. camadulensis (3.55), T. grandis (3.48), H. binata (3.44), and T. arjuna (3.33) plantations. The index of equitability varied narrowly from 0.9 Diversity can be studied at three different scales to 0.99. The coefficient of dominance showed inverse relationship with the diversity index and ranged between 0.02 and 0.09. The maximum value (0.09) was recorded in A. Alpha diversity auriculiformis plantation, followed by T. grandis (0.08), H. binata and T. arjuna (0.07), E. camadulensis (0.06), A. procera (0.02). The species richness recorded wide variation from The species diversity within a community or habitat that an average of 18 to 42, with the maximum in A. procera, responds to a balance between the actions of local biotic followed by A. auriculiformis, E. camadulensis, T. grandis, H. and abiotic elements and immigrations from other binata, and T. arjuna plantation. The density also recorded locations. This comprises of two components, i.e. large variation among different plantations varying from an species richness and evenness that can be measured average of 274 to 758.8 plants m-2, with the maximum in T. by variety of indices even including taxonomic diversity grandis and the minimum in T. arjuna plantation. The standing crop biomass varied from 60.2 to 288 g dw m-2 among different in Avalanch index (Ganeshaia 1998). plantations without any notable seasonal differences. High values of indices of diversity and equitability may denote good Beta diversity ecological health of these plantations. A high turnover of species together with low similarity between plantations in a small geographical area indicates high sustainability status It is the inter community diversity that expresses the rate of the plantations. of species turnover per unit change in habitat and can be assessed by a variety of indices (Schluter and Ricklefs
136 1993). is both reduced in area and divided into two or more fragments. Habitat fragmentation may limit the potential of species for dispersal and colonization. It also reduces Gama diversity the foraging ability of animals. It causes such edge effects as microclimatic changes in light, temperature, The overall diversity at the landscape level that includes wind, etc. a and b diversities (Schluter and Ricklefs 1993). A significant loss is also taking place due to introduction of exotic species, high levels of disease Threats to Biodiversity incidence and excessive exploitation of particular species by people, and shift or Jhum cultivation, etc. Human activities are directly and indirectly responsible Ground flora is an important component of forest for current high rates of biodiversity loss. Some of the floor because it provides a wide array of goods and major issues are: services to the ecosystem in addition to its valuable gene Habitat loss, fragmentation and degradation result diversity. It conserves soil, maintains nutrients, provides due to agricultural activities, extraction (i.e mining, fishing, crucial support to wild animals and supplies fodder, food logging and harvesting) and development (i.e. human and medicines to humans. Diversity and production of settlements, industry and associated infrastructure). ground flora under plantation forests vary with the age Habitat loss and fragmentation lead to the formation of and type of tree species, soil structure and climate isolated, small, scattered populations. These small (Mathur and Soni 1983; Singh et al. 1986; Rajvanshi et populations are increasingly vulnerable to inbreeding al.1983; Singh et al. 2001; Sharma et al. 2002; Khanna depression, high infant mortality and are susceptible to et al. 1989). stochastic environmental events, and consequently, Various workers, in different forest ecosystems possible extinction. Changes in forest composition and throughout the world, have conducted studies on plant quality, and the resultant habitat type lead to decline in diversity. Monk (1967) and Risser and Rice (1971) have primary food species for wildlife due to following reasons found diversity indices ranging from 1.49 to 3.40 for (SoE 2009): deciduous forests of North America and for temperate forests of New world. Knight (1975) reported diversity Poaching and hunting • index value of 5.06 to 5.40 for tropical forests. Simpson • Invasive species (1964) has attributed the lower diversity and greater • Over-exploitation of wild bio resources concentration of dominance in temperate vegetation to • Pollution of atmosphere, water and soil lower rates of evolution and diversification of • Global climate change communities. Whittaker (1975) proposed the niche preemption hypothesis for dominance diversity curves. According to him, the geometric forms of the dominance Habitat destruction diversity curves of vascular plant communities show low diversity. The primary causes of loss of biodiversity is not direct Species diversity and vegetation structure in human exploitation but habitat destruction that inevitably tropical dry deciduous forest ecosystems of India have results from the expansion of human population and been studied in details by several workers (Shah et al. activities in many countries particularly on island and 1978; Shah and Bhatt 1980; Verma and Das 1981; where human population density is high. Most of the Banerjee and Lal 1985; Sharma et al. 1986; George and original habitat has been destroyed. More than 50% for Verghese 1993; Khare et al. 1989; Choudhary et al. wildlife habitat has been destroyed in 49 out of 61 old 1986). world tropical countries (IUCN, UNEP, GEF 2001) An ecological equilibrium, however, is never static and is a dynamics process (Huston 1994). Within each Habitat fragmentation community, there is circulation of matter and flow of energy resulting in dynamics equilibrium. It is not an Habitat that formerly occupied wide areas are now often actual equilibrium but always has a tendency to re- divided into pieces by roads, agriculture fields, towns, establish if left undistributed. Unfortunately, human canals, power lines, etc. The continuous area of habitat interference does not leave it as such. Studies dealing with distribution of individual species of various classes,
137 association between species, pattern of dispersion and 1. To asses the surface herb flora under plantation of various indices of diversity are considered very important different tree species. in understanding three dimensional structure of the 2. To study the seasonal variation in surface herb flora forest. Plant diversity is enhanced during the later stages under plantation forests. of succession with maximum in mid succession (Huston 1994). Spatial variation in soil water, nutrients or texture is almost always reflected by pattern in the communities Material and methods of plant and other organisms growing on the soil. Diversity in early succession can be high or low, depending on Study site propagule input, soil nutrient availability and their affect on growth rates and competitive interactions. Diversity does not always increase with productivity or the Six plantations (95.1ha) located in the campus of the environmental conditions that influence productivity. State Forest Research Institute situated on the southern However, productivity can be either negatively or fringe of Jabalpur city with coordinal points 790 56' E positively correlated with species diversity (Huston 1994). Longitude and 23010' N Latitude, 400 on AMSL were chosen for study. The topography of the area was No systematic information is available on undulating with lush green natural vegetation. The the population dynamics of herbs in man- made climate of Jabalpur is governed by the monsoon pattern ecosystems. Hence, studies were undertaken to (1403.72 mm) and the year may be divided into three determine spatial and temporal patterns of diversity with season, viz. summer (March to mid June), winter following objectives: (November to February) and rainy (mid June and end in October) (Fig.1). The present study was under taken in Table 1. Description of plantations Common Botanical name Year Compartment Area Number of trees ha-1 name number (ha) Originally Surviving % raised
Acacia Acacia auriculiformis 1978 24 0.4 720 593 82 Teak Tectona grandis 1978 5 0.38 658 384 58 Anjan Hardwickia binata 1980 8 0.21 2100 1533 73 Eucalyptus Eucalyptus camadulensis 1981 9 0.47 834 453 54 Albizia Albizia procera 1978 11 0.34 594 268 45 Arjun Terminalia arjuna 1978 40 0.20 1250 515 41
Fig.1. Location of study site
138 the six plantations (Table 1). 161 species belonging to 114 genera and 37 families. A. procera plantation comprised of a total 161 species a Diversity was measured by Shannon- Weiner index of belonging to 106 genera and 39 families. T. arjuna diversity (H) (Magurran 1988), McIntosh index of species plantation comprised of a total 178 species belonging to richness (S) (McIntosh 1967), and Simpson’s index of 119 genera and 44 families. Among different plantations, dominance (Simpson 1949). T. arjuna recorded the maximum species richness (178 (1) Shannon Weiner index of diversity (H) species, 119 genera and 44 families), and H. binata recorded the minimum species richness (155 species, H = - Pl P i i n i 108 genera and 34 families). The herbaceous flora of
Where, Pi = ni/N. ni is the number of individuals of a all the plantations consisted of a total 281 species species and N is the total number of individuals of all the belonging to 171 genera and 58 families (Table 2). Sonkar species. et al. (2005) have registered changes in ground flora (2) McIntosh index of species richness (S) diversity and productivity due to plantation of six tree S= Total number of species species, viz. Azadirachta indica (Neem), Bauhunia vahlii (3) Simpson’s index of dominance (Cd) (Kachnar), Eucalyptus camadulensis (Eualyptus), Cd= P 2 Emblica officinalis (Anola) and Pongamia pinnata i i (Karang), raised in degraded land in Karaboh area in (4) Shannon- Weiner index of equitability (J) East forest Division, Chhindwara (M.P.). After six years H of planting, the ground flora consisted of 21 species in J = ——- E. officinalis, followed by 17 in P. pinnata, 16 in B. vehlai lns and 14 each in A. indica and E. camadulensis plantations. b Diversity was calculated by Sorenson’s Similarity index (1) Sorenson’s similarity index (S.I.) Table 2. Floristic composition of surface herb flora in The Similarity index between pairs of communities was different plantations at State Forest Research calculated following Sorenson’s formula (Gupta and Institute Jabalpur Shukla 1991) 2C Name of the plantation Families Genera Species Similarity index = ———— x 100 A. auriculiformis 43 107 162 A+B T. grandis 37 106 160 H. binata 34 108 155 Where, E. camadulensis 37 114 161 A = Total number of species in Community A A. procera 39 106 161 B = Total number of species in Community B T. arjuna 44 119 178 C= Total number of species common to both the Total 58 171 281 Communities Poaceace recorded the maximum number of genera (34) and species (51), was followed by Fabaceae Result and Discussion with 20 species and 9 genera; Asteraceae, Euphorbiaceae, Malvaceae each with 17 species Floristic composition of surface ground flora belonging to 12, 5 and 7 genera respectively; Cyperaceae (15 species and 4 genera); Lamiaceae and The distribution of surface ground flora of 25 years old Papilionaceae with12 species each belonging to 9 and six different tree species, viz. A. auriculiformis, T. grandis, 6 genera, respectively, Tiliaceae (10 species and 2 E. camadulensis, H. binata, A. procera and T. arjuna, genera); Acanthaceae (9 species and 7 genera); exhibited significant variations in numbers of species Amaranthaceae (8 species and 4 genera); (Table 2). The herbaceous flora of A. auriculiformis Commelinaceae (6 species and 5 genera); plantation comprised of a total of 162 species belonging Cesalpinaceae, Oxaliadaceae, Rubiaceae, to 107 genera and 43 families. T. grandis plantation Scorophularaceae with 5 species belonging to 1, 3, 3 comprised of a total 160 species belonging to 106 genera and 2 genera, respectively; Convolvulaceae and and 37 families. H. binata plantation comprised of a Dioscoreaceae with 4 species each belonging to 2 and total 155 species belonging to 108 genera and 34 1 genera, respectively; Apocynaceae, Asclepiadaceae, families. E. camadulensis plantation comprised of a total Cucurbiataceae, Gentianaceae, Polygonaceae and Solanaceae with 3 species each belonging to 3, 3, 3, 2,
139 3, 2 genera, respectively; and Basellaceae, Plumbaginaceae, Portulacaceae, Rutaceae, Smilaceae, Cappariadaceae, Caryophyllaceae, Liliaceae, Sterculiaceae, Tamaricaceae, Verbenaceae, Vitidaceae Menispermaceae, Mimosaceae, Zingiberaceae with 2 and Zygophyllaceae (Table 3). species each belonging to 1, 2, 2, 2, 2, 2, 1 genera, respectively. Twenty seven families were represented Index of diversity by only 1 species and 1 genera, viz. Apiaceae, Araceae, Aristolochiaceae, Averrhoaceae, Begoniaceae, Boraginaceae, Celastraceae, Ceratophyllaceae, The index of diversity increased during the winter season, Chenopodiaceae, Costaceae, Elatinaceae, Leeaceae, decreased during the summer season, and increased Lythraceae, Malpighiaceae, Martyniaceae, again during the rainy season. Such a seasonal trend Nyctaginaceae, Orchidaceae, Papaveraceae, was observed in all the plantations (Fig. 3). In A.
Table 3. Distribution of families, genera and species of surface herb flora in all the plantations at State Forest Research Institute Jabalpur
Name of Family Number of Name of Name of Family Number of Name of species genera species genera Acanthaceae 9 7 Leeaceae 1 1 Amaranthaceae 8 4 Liliacece 2 2 Apiaceae 1 1 Lythraceae 1 1 Apocynaceae 3 3 Malpighiaceae 1 1 Araceae 1 1 Malvaceae 17 7 Aristolochiaceae 1 1 Martyniaceae 1 1 Asclepiadaceae 3 3 Menispermaceae 2 2 Asteraceae 17 12 Mimosaceae 2 2 Averrhoaceae 1 1 Nyctaginaceae 1 1 Basellaceae 2 1 Orchidaceae 1 1 Begoniaceae 1 1 Oxalidaceae 5 3 Boraginaceae 1 1 Papaveraceae 1 1 Capparidaceae 2 2 Papilionaceae 12 6 Caryophyllaceae 2 2 Plumbaginaceae 1 1 Ceasalpinaceae 5 1 Poaceae 51 34 Celastraceae 1 1 Polygonaceae 3 3 Ceratophyllaceae 1 1 Portulacaceae 1 1 Chenopodiaceae 1 1 Rubiaceae 5 3 Commelinaceae 6 5 Rutaceae 1 1 Convolvulaceae 4 2 Scrophulariaceae 5 2 Costaceae 1 1 Smilaceae 1 1 Cucurbitaceae 3 3 Solanaceae 3 2 Cyperaceae 15 4 Sterculiaceae 1 1 Dioscoreaceae 4 1 Tamaricaceae 1 1 Elatinaceae 1 1 Tiliaceae 10 2 Euphorbiaceae 17 5 Verbenaceae 1 1 Fabaceae 20 9 Vitidaceae 1 1 Gentianaceae 3 2 Zingiberaceae 2 1 Lamiaceae 12 9 Zygophyllaceae 1 1
Table 4. Similarity index matrix ( diversity) of herbaceous flora among different plantations at State Forest Research Institute Jabalpur A. auriculiformis T. grandis H. binata E. camadulensis A. procera T. arjuna A. auriculiformis - 0.72 0.64 0.62 0.69 0.61 T. grandis 0.72 - 0.68 0.59 0.39 0.54 H. binata 0.64 0.68 - 0.61 0.58 0.49 E.camadulensis 0.62 0.59 0.61 - 0.53 0.63 A. procera 0.69 0.39 0.58 0.53 - 0.43 T. arjuna 0.61 0.54 0.49 0.63 0.43 -
140 4 4
3 3
A. auriculiform is T. grandis 2 2 J u l J u l O c t O c t A p r A p r J a n M a r J a n M a r F e b J u n F e b J u n S e p D e c S e p D e c N o v M a y A u g N o v M a y A u g
4 4
3 3
H. binata E. camadulensis 2 2 Jul Jul Oct Oct Apr A pr Jan Mar Jan Mar Feb Jun Feb Ju n Sep D ec S ep D ec Nov May A ug N o v May A u g
4 4
3 3
A . procera 2 T. arjuna 2 J u l O ct J u l A p r J an M ar F e b J u n O c t S e p D ec A p r N o v M a y A u g J a n M a r F e b J u n S e p D e c N o v M a y A u g
Fig. 3. Monthly variation in Shannon-Weiner index of diversity of ground flora in different plantations (Values are mean ± standard deviation)
1 .0 1.0
0 .9 0.9
A. auriculiformis T. grandis 0 .8 0.8 Jul Ju l Oct A pr Jan Mar Oct Feb Jun A p r Sep D ec Jan M ar N ov May A ug F eb Ju n S e p D e c N o v M ay A u g
1.0 1.0
0.9 0.9
H. binata E. camadulensis 0.8 0.8 Jul Jul Oct Oct Apr A pr Jan Mar Jan Mar Feb Jun Feb Jun Sep Dec S ep D ec Nov May Aug N ov May A ug
1 .0 1.0
0 .9 0.9
A. procera T. arjuna 0 .8 0.8 Jul Ju l Oct A pr Jan Mar Oct Feb Jun A p r Sep D ec J an M ar N ov May A ug F eb Ju n S ep D e c N o v May A u g
Fig. 4. Monthly variation in Shannon-Weiner index of equitability of ground flora in different plantations (Values are mean ± standard deviation)
141 0.2 0 .2
0.1 0 .1
A. auriculiform is 0.0 T. grandis 0 .0 Jul Oct A p r Jan M ar J u l F eb Jun S ep D ec N ov M ay A ug O c t A p r J a n M a r F e b J u n S e p D e c N o v M a y A u g
0 .2 0 .2
0 .1 0 .1
H. binata E. camadulensis 0 .0 0 .0 J u l J u l O c t O c t A p r A p r J a n M a r J a n M a r F e b J u n F e b J u n S e p D e c S e p D e c N o v M a y A u g N o v M a y A u g
0 .2 0 .2
0 .1 0 .1
A.procera T. arjuna 0 .0 0 .0 J u l J u l O c t O c t A p r A p r J a n M a r J a n M a r F e b J u n F e b J u n S e p D e c S e p D e c N o v M a y A u g N o v M a y A u g Fig. 5. Monthly variation in Simpson index of dominance of ground flora in different plantations (Values are mean ± standard deviation)
5 0 5 0
4 0 4 0
3 0 3 0
2 0 2 0
1 0 1 0 A . auriculiformis T. grandis 0 0 J u l J u l O c t A p r O c t J a n M a r A p r F e b J u n J a n M a r S e p D e c F e b J u n N o v M a y A u g S e p D e c N o v M a y A u g
5 0 5 0
4 0 4 0
3 0 3 0
2 0 2 0
1 0 1 0 H. binata E. camadulensis 0 0 J u l O c t J u l A p r J a n M a r F e b J u n O c t S e p D e c A p r N o v M a y A u g J a n M a r F e b J u n S e p D e c N o v M a y A u g
5 0 5 0
4 0 4 0 3 0 3 0 2 0 2 0 1 0 1 0 T. arjuna 0 A . procera 0 J u l O c t A p r J a n M a r F e b J u n S e p D e c N o v M a y A u g J u l O c t A p r J a n M a r F e b J u n S e p D e c N o v M a y A u g
Fig. 6. Monthly variation in species richness of ground flora in different plantations (Values are mean ± standard deviation
142 8 0 0 8 0 0
6 0 0 6 0 0 - 2 - 2
4 0 0 4 0 0 p la n ts m p la n ts m 2 0 0 2 0 0 A . auriculiform is T. grandis 0 0 J u l J u l O c t A p r O c t A p r J a n M a r F e b J u n J a n M a r F e b J u n S e p D e c S e p D e c N o v M a y A u g N o v M a y A u g
8 0 0 8 0 0
6 0 0 6 0 0 - 2 - 2
4 0 0 4 0 0 p la n ts m p la n ts m 2 0 0 2 0 0 H. binata E. camadulensis 0 0 J u l J u l O c t A p r O c t J a n M a r A p r F e b J u n J a n M a r S e p D e c F e b J u n N o v M a y A u g S e p D e c N o v M a y A u g
8 0 0 8 0 0
6 0 0 6 0 0 - 2 - 2
4 0 0 4 0 0 p la n ts m p la n ts m 2 0 0 2 0 0 A. procera T. arjuna 0 0 J u l J u l O c t O c t A p r A p r J a n M a r J a n M a r F e b J u n F e b J u n S e p D e c S e p D e c N o v M a y A u g N o v M a y A u g
Fig. 7. Monthly variation in density (plants m-2) of ground flora in different plantations (Values are mean ± standard deviation)
2 5 0 2 5 0
2 0 0 2 0 0 - 2 - 2 1 5 0 1 5 0
1 0 0 1 0 0 g d w m g d w m 5 0 5 0 A. auriculiformis T. grandis 0 0 J u l J u l O c t O c t A p r A p r J a n M a r J a n M a r F e b J u n F e b J u n S e p D e c S e p D e c N o v M a y A u g N o v M a y A u g
2 5 0 2 5 0
2 0 0 2 0 0 - 2 - 2 1 5 0 1 5 0
1 0 0 1 0 0 g d w m g d w m 5 0 5 0 H. binata E. camadulensis 0 0 J u l J u l O c t A p r J a n M a r O c t F e b J u n A p r S e p D e c J a n M a r N o v M a y A u g F e b J u n S e p D e c N o v M a y A u g
2 5 0 2 5 0
2 0 0 2 0 0 - 2 1 5 0 1 5 0
1 0 0 1 0 0 g d w m g d w m -2 5 0 5 0 A. procera T. arjuna 0 0 J u l J u l O c t A p r O c t J a n M a r A p r J a n M a r F e b J u n S e p D e c F e b J u n S e p D e c N o v M a y A u g N o v M a y A u g
Fig. 8. Monthly variation in standing crop biomass (g dw m-2) of ground flora in different plantations (Values are mean ± standard deviation)
143 auriculiformis plantation, Index of diversity ranged from 0.03 to 0.08 in A. procera, and 0.04 to 0.14 in T. arjuna 1.98 to 3.66, with the maximum being observed during plantation. the period from October to February and the minimum from April to June. The area variability (measured as Species richness ratio of standard deviation to mean) was about 10% during the months of April, May and September, and less than 5% in the remaining months. In T. grandis The species richness recorded similar seasonal pattern plantation, it ranged from 1.68 to 3.57, with the maximum in different plantations, i.e. it increased during the winter, being recorded during the period from October to decreased sharply during the summer and increased February and the minimum from March to May. The again during the rainy season (Fig. 6). The maximum area variability was about 10% during the months of species richness was observed during the winter and March, April, May, and September and less than 5% the minimum during the summer season. It changed during the remaining months. In H. binata plantation, it from 8 to 43 in A. auriculiformis, 6 to 38 in T. grandis, 12 ranged from 2.19 to 3.51, with the maximum being to 37 in H. binata, 10 to 41 in E. camadulensis, 7 to 44 recorded during the period from November to February in A. procera, and 9 to 35 in T. arjuna plantation. The and the minimum from March and October. The area plantations like A. auriculiformis and A. procera recorded variability was always less than 10%. In E. camadulensis comparatively higher turnover of species as compared plantation, it ranged from 2.15 to 3.62, with the maximum with that recorded in the remaining plantations. being recorded from October to February and the minimum from March and May. The area variability was about 10% during the months of March, April, June and Similarity index September and less than 5% in the remaining months. In A. procera plantation, it ranged from 2.13 to 3.68, with The index of similarity of the surface herb flora in the A. the maximum being observed from November to auriculiformis with that of the other plantations was in February and the minimum during March, April and the close range of 0.61 to 0.72, recording the maximum September. The area variability was about 10% during similarity with T. grandis and the minimum with T. arjuna the months of March, April and September and less than plantation (Table 4). The similarity index of T. grandis 5% in the remaining months. In T. arjuna plantation, it with other plantations varied widely from 0.39 to 0.72, ranged from 2.05 to 3.44, with the maximum being having the maximum similarity with A. auriculiformis and recorded during the period from November to February minimum similarity with A. procera. The index of similarity and the minimum during March, July and October. The of H. binata plantation with that of other plantations was area variability was at least 10% during October, March, in the range of 0.49 to 0.68, showing high similarity with April and July and less than 5% in the remaining months A. auriculiformis and T. grandis and low with T. arjuna. (Fig. 3). The herb flora of E. camadulensis plantation recorded a narrow range of similarity index (0.53 to 0.63) with other Index of equitability plantations. The index of similarity of A. procera with other plantations was in a broad range of 0.39 to 0.69, being the most similar with and the least The index of equitability did not record any significant A. auriculiformis similar to and The similarity index monthly variation in different plantations (Fig. 4). It ranged T. grandis T. arjuna. of T. arjuna plantation vis-a- vis other plantations was in from 0.84 to 1.0 in A. auriculiformis, 0.82 to 11.0 in T. the range of 0.43 and 0.63 (Table 4). The floristic grandis, 0.91 to 0.99 in H. binata, 0.92 to 0.99 in E. composition of surface herb flora recorded in camadulensis, 0.87 to 0.99 in A. procera, and 0.88 to A. was highly similar to that of the other 1.0 in T. arjuna plantation. The values were generally auriculiformis less than 0.9 during summer and more than 0.9 during plantations, but that of A. procera was the least similar rainy season. to that of the other plantations. Generally, the herbaceous flora of neighboring plantations, and even of barren lands, is reported to be highly similar by Verma Index of dominance et al. (2000), Pandey et al. (1988), Kunhikanan et al. (1998), Singh et al. (2004), Sonkar et al. (2005). The index of dominance increased during summer and decreased during winter season in all the plantations Density (Fig. 5). It was always less than 0.15. It varied from 0.1 to 0.15 in A. auriculiformis, 0.1 to 0.13 in T. grandis, 0.03 to 0.11 in H. binata, 0.03 to 0.13 in E. camadulensis, The standing crop density of surface herb flora increased
144 sharply during the winter, decreased rapidly during the summer seasons. Studies have also been conducted summer, and increased gradually during the rainy on floral diversity in man made forest ecosystems season. The herb density was the maximum in the winter (Sonkar et al. 2005; Singh et al. 2001). and the minimum in the summer months (Fig. 7). Such a seasonal trend was observed in all the plantations. Higher diversity index and lower coefficient of The herb density varied widely from 108 to 744 plants dominance may indicate the stability of community (Mc Intosh 1967). In this regard, the ground flora community m-2 in A. auriculiformis, 46 to 840 plants m-2 in T. grandis, of may be considered more stable than others. 168 to 728 plants m-2 in H. binata, 148 to 680 plants m-2 A. procera Similar results were reported reported by Sonkar in E. camadulensis, 84 to 828 plants m-2 in A. procera, et.al (2005). The index of equitability of the herbaceous and 44 to 664 plants m-2 in T. arjuna plantation. ground flora did not show much variation in different plantations, which may be explained by similar climatic Standing crop biomass conditions of the study area. There was a large variation in index of species richness varying from an average of The standing crop biomass of surface herb flora recorded 18 to 42. The ground flora witnessed a very high turnover insignificant seasonal variations but significant periodic of the species according to the period, which was changes within each month in all the plantations (Fig. comparatively faster in A. proceri relative to that observed 8). The biomass was the maximum in summer and the in other plantations. These results clearly denoted that minimum in rainy months. It varied narrowly between life-form of the dominant tree species regulated the type and density of surface ground herbaceous flora. This 84.8 and 239.6 g dw m-2 in A. auriculiformis, 83.2 and may explain the observed insignificant differences in 241 g dw m-2 in T. grandis, 80 to 238 g dw m-2 in H. density and biomass of ground flora among different binata, 81.2 to 239.6 g dw m-2 in E. camadulensis, 80.4 plantations together with their distinct floristic composition and 288.4 g dw m-2 in A. procera, and 60.2 and 239.8 g and species turnover rates. The highly diverse and dw m-2 in T. arjuna plantation. The herbaceous biomass did not record significant differences among different ecologically healthy ground flora may facilitate self- plantations. sustenance of artificial forest plantations after threshold limits of time. High values of Shannon-Weiner index of diversity (~3.0) and equitability (~1.0) strongly indicated towards the sound ecological state of the plantations. The index ekuo fufeZr 25 o"kksZ iqjkus o`{kkjksi.k esa 6 fofHkUu iztkfr;ksa of diversity varied narrowly from 2.72 to 3.64 with the vdsf’k;k] lkxkSu] vatu] ;wdsfyfIVl] vYcsft;k rFkk vtqZu esa maximum (3.64) being noted in A. procera followed by tehuh ouLifr;ksa dh tula[;k dk rqyukRed v/;;u fd;k x;k A that in A. auriculiformis (3.59), E. camadulensis (3.55), v/;;u ds nkSjku lHkh jksi.k o`{kksa esa 281 tehuh ouLifr] 171 T. grandis (3.48), H. binata (3.44), and T. arjuna (3.33) plantations. The coefficient of dominance of herb tsusjk rFkk 58 dqy ik;s x;s A lokZf/kd tehuh ouLifr vtqZu esa species showed inverse relationship with the diversity rFkk vatu esa lcls de ik;s x;s A tehuh ouLifr;ksa dh fofo/krk index and ranged between 0.02 and 0.09. The maximum ladsrd esa lokZf/kd cnyko ns[kk x;k 2-72 ls 3-74 A tehuh value (0.09) was recorded in A. auriculiformis plantation, ouLifr;ksa dh lekurk ladsrd esa de cnyko ns[kk x;k 0-90 ls followed by T. grandis (0.08), H. binata and T. arjuna 0 99 (0.07), and E. camadulensis (0.06). - A tehuh ouLifr;ksa dh xq.kkad izcyrk esa fofo/krk ladsrd ds vuq:i foijhr izHkko ns[kk x;k A tehuh ouLifr;ksa dh iztkfr Kumar et al. (2001) have observed seasonal 18 42 dynamics in the plant biodiversity and phytosociology of miyC/krk esa vf/kd cnyko ik;k x;k ls A lokZf/ ground flora in Acacia auriculiformis plantation stands kd vYcsft;k esa rFkk lcls de vtqZu ds o``{kksa esa ikbZ xbZ A tehuh at two sites in Varanasi. Maximum plant growth in the ouLifr;ksa ds ?kuRo esa lokZf/kd cnyko ns[kk x;k 274 ls 758-8 region was recorded during July to October, with ikS/k m-2 A tehuh ouLifr;ksa ds tSo Hkkj esa 60-2 ls 288 g dw appearance of annuals completing their life cycle by 2 October. Between two sites, the number of species at m- ik;k x;k A lokZf/kd fofo/krk rFkk lekurk ladsrd LoLFk site- I was greater. Variability in numbers was noticed o`{kkjksi.k dks n’kkZrs gSa A de HkkSxksfyd {ks= esa lekurk o`{kkjksi.k esa that was attributed to longer light protection, higher lokZf/kd ouLifr;ksa esa ifjorZu mPp xq.koÙkk o`{kkjksi.k dks n’kkZrk gS canopy coverage and higher litter fall deposition on the A forest floor. Due to ombrothermic changes in different seasons, the vegetation of plantation stands was luxuriant in rainy and winter season while sparse during References
145 Banerjee K L B, Lal P (1985) Vegetation of the little known certain concepts of diversity. Ecology 48: 392-404 District Seoni in Madhya Pradesh. Indian J Fore 8: Monk C D (1967) Tree species diversity in eastern deciduous 292-297 forest with reference North Central Florida. Amer Chaudhary R (1986) Fire in bamboo area -Lessons from Naturalist 101: 173-187 Tadoba Park. Indian Fore 112: 900-907 Pandey P K, Bist A P S, Sharma S C (1988) Comparative Convention of Biological diversity (1992). vegetation analysis of some plantation ecosystems. Ganeshaiah K N, Shankar Uma R, Murali K S, Shankar Uma, Indian Fore 114: 379-389 Bawa J S (1998) Extraction of Non Timber forests Rajvanshi A, Soni S, Kukreti U D, Shrivastava M M (1983) A products in the forest of Biligiri Rangan Hills India -5 comparative study of under growth of sal forest and influence of dispersal mode on species response to ecalyptus plantations at Golatpur Dehradun during anthropogenic pressure. Econ Bot 52 : 316- 319 rainy season. Indian J For 6:117-119. George M, Verghese G (1993) Species diversity and structural Risser P G, Rice E L (1971) Diversity in tree species in variation of tropical dry deciduous forest ecosystem Oklahoma upland forests. Ecology 52: 876-888 of Western Ghats. Indian J Trop Biod 1: 30-36 Schluter D, Ricklefs R E (1993) Species diversity in Ecological Gupta O P and Shukla R P (1991) The composition and communities. The University of Chicago press dynamic of associated communities of Sal Chicago plantations. Trop Ecol 32: 296-309 Shah G L, Yadav S S, Paraabia M H (1978) Phytosociology Huston M A (1994) Biological diversities. Cambridge University studies on vegettion of Chhota Udapur Forest Press London Division, Eastern Gujarat. Indian J For 1: 312-318 IUCN – The International Union for the Conservation of Nature Shah G L, Bhatt R G (1980) Phytosociology of Panchmahals and Natural Resources The Integration of district in Eastern Gujarat. Indian J For 3:47-53 Biodiversity into National Environmental Assessment Sharma N (2002) Floral diversity in relation to soil in some Procedures-National Case Studies-India. 2001. selected stress site plantations. Ph.D. Thesis F R I UNDP/ UNEP/ GEF (Deemed University) Dehradun Khanna L S (1989) Principle and Practice of Silviculture. Sharma S K, George M, Prasad K G, Balasundaram A (1986) Khanna Bandhu Dehradun Cholakarai- a confluence of two vegetation types. Khare PK, Yadav VK, Mishra GP (1989) Phytosociological Indian J Fore 9: 47-53 structure of some forest communities in central India Simpson E H (1949) Measurement of diversity. Nature 163: - two level ordination. J Trop For 5: 321-328. 688 Knight DH (1975) A phyto - sociological analysis of species Simpson G G (1964) Species diversity of north American rich tropical forest of Barrocolorado island Panama. Recent Mammals. Syst Zool 13: 5573. Ecol Monogra 45: 259-284 Singh A K, Sharma N, Kunhikannan C, Banerjee S K Kumar J, Singh M P (2001) Seasonal dynamics in the plant (2001) Effect of spacing on ground flora diversity biodiversity and phytosociology of ground flora in and productivity in a Dalbegia sisoo plantation on Acacia auriculiformis plantation stands. Indian Fore degraded land. J. Trop For 17: 49-58. 127:130-134 Singh AK, Prasad, Singh R B (1986) Availability of phosphorus Kunhikannan C, Verma R K, Verma Raj K, Khatri P K, Totey and potassium and its relation with some important NG (1988) Ground for a soil microflora and fauna physico-chemical properties of some forest soils of diversity under plantation ecosystem on Bhata land Pali Range Shadol (M P ). Indian Fore 112: 1094- of Bilaspur Madhya Pradesh. Environ Ecol 16: 529- 1103 548 Singh A K, Sharma N, Lal H, Shukla P K (2004) Effects of Magurran A E (1988) Ecological Diversity and its planting geometry on ground flora diversity in Bursera Measurement. Princeton University Press New penicillata plantation. Trop Fore 20: 21-38 Jersey Sonkar S D, Tripathi D P, Nandeswar D L, Nain N P S (2005) Mathur H N, Soni P (1983) Comparative account of Ground flora diversity and productivity under the undergrowth under Eucalyptus and Sal in three plantations of different tree species in degraded land. different localities of Doon Valley. Indian Fore 109: (ManuscriptTrop Received: Biodiversity 30.05.2011; 13: 20-28. Accepted: 30.06.2011) 882-890 State of Environment report Ministry of Environment and McIntosh R P (1967) An index of diversity and the relation of Forests, Government of India, 2009 Verma R K, Das R R (1981) Ecological studies on the forest of Kolaras range. Indian J For 4: 123-128 Verma R K, Shadangi D K, Totey N G (2000) Effect of Dalbergia sissoo (Roxb.) and Dendrocalamus strictus (Roxb.) plantations on enhancement of biological diversity in degraded land. Trop Fore 16: 36-44 Whittaker R H (1975) Communities and Ecosystem Mc Millan- New York : 385 146 JNKVV Res J 45(2): 147-149 (2011)
Population dynamics of parasitic nematodes in soybean grown under climatic conditions of Kymore Plateau and Satpura Hills of Madhya Pradesh
S.P. Tiwari and Jayant Bhatt Department of Plant Pathology Jawaharlal Nehru Krishi Vishwa Vidyalaya Jabalpur 482004 (MP)
Abstract Material and methods
Spiral nematode , Helicotylenchus spp. was predominant and A total of 214 soil samples were collected from 155 widely distributed in the rhizosphere of soybean and population localitions using 2.5 diameter soil probes, from a depth ranged from 25 to 2550/200 cm3 soil in six districts of Kymore of 50 cm areas cropped with soybean crop that exhibited Plateau and Satpura Hills viz., Chhindwara, Jabalpur, symptoms of nematode infestations at initial visual stage. Narsingpur, Rewa, Satna and Seoni. Lesion nematode Composite samples of soil and root were collected from Pratylenchus spp, was encountered in 81 localities with the mean population of 395 nematode /200 cm3. Crop was each field of Chhindwara (28), Jabalpur (36), Narsingpur severely infested with Rotylenchulus reniformis in Fanwani (40), Rewa (30), Satna (35), and Seoni (45). village of Jabalpur district which harboured 1500 nematdes/ The composite homogenized 200 cm 3 soil 200 cm3 soil. Second stage Juveniles of Heterodera spp. were recorded from few soil samples from Rewa and Satna. Other samples were processed by using wet sieving and nematode viz., Tylenchorhynchus spp., Ditylenchus spp., decanting technique (Christie and Perry 1951). Extracted Hoplolaimus indicus, Aphelenchus avenae and Xiphinema nematodes were counted by using a Hawksley counting spp. were also observed in rhizosphere of soybean. slide and identified as per characters provided by Siddqui (1986). Keywords: Soybean, nematode, Helicotylenchus, Pratylenchus, soil population, frequency Results and Discussion
Cultivation of soybean during 2009-10 in Kymore Plateau Two hundred fourteen soil samples yielded 11 different and Satpura Hills of Madhya Pradesh has exceeded nematode genera and most of the samples contained above 4.5 thousand hectares. The socio-economically more than one species and genus. Lesion nematode, important oilseed crop is exposed to attack by plant (Pratylenchus spp.) the most common and widely parasitic nematodes inflicting 10 percent loss in yield occurring potential migratory endoparasite was (Feldmesser 1971). Root knot, soybean cyst, lance, associated with soybean rootlets from 81 localities under sting, spiral, root lesion and stubby root are often the survey with the mean population 395/200 cm3 soil. associated and responsible for severe damage to The frequency of occurrence was 62.5% as recorded in soybean under field conditions (Revios and Golden samples from Seoni followed by Satna (52.17 %) and 1987).Ultimately providing the passage to secondary Jabalpur (51.35%) districts (Table 2 and Fig 2). Samples intruders information on the distribution of Plant parasitic of soybean from Tilsani, expressed poor vigour along nematode associated with soybean in Madhya Pradesh with necrotic roots holding 3000 nematodes / 200 cm3 is not available. Therefore, present investigations were also, however, fields of Chourai (2500). Parasiya (1625) undertaken to record plant parasitic nematodes their and Baghraji (1400) showed poor crop growth. Thirty occurrence and distribution in agro-ecosystem of Kymore nine localities harboured average nematode population Plateau and Satpura Hills of the state which occupy major between 201-500, whereas nematode from 12 localities area under Soybean cultivation. 501-1000 and from four localities above 1000 nematode. Revfios and Golden (1978) reported lesion and cyst
147 Table1. Frequency, Standard deviation and Population ranges of nematode associated with soybean in Kymore Plateau and Satpura Hills Associated genera Population in different class intervals Nematode population/ Standard ( No. of Localities) 200 cm3 range deviation I II III IV Total Min. Max. Mean
Helicotylenchus spp. 37 63 34 19 153 25 2525 1437 11033 Hoplolaimus indicus 33 16 05 02 56 25 1850 281 297 Pratylenchus spp. 35 39 12 04 90 25 3000 394 476 Rotylenchulus reniformis 06 14 04 01 25 50 1950 440 401 Tylenchorhynchus spp. 16 09 01 02 28 25 400 126 126 Xiphinema spp 41 11 03 04 59 25 900 161 133 Other Nematodes Aphelenchus avenae 08 25 06 00 39 50 900 384 254 Tylenchus & related genera 26 28 07 07 68 75 2450 452 429 Ditylenchus spp. 11 02 0 0 13 25 400 98 113 Longidorus spp. 03 01 0 0 04 25 90 275 318 Neotylenchus spp. 06 02 0 0 08 75 225 150 70 *Class intervals (frequency) I =25-200; II=201-500; III=501-1000; IV= above 1000 Nematodes / localities
Table 2. District wise distribution of Plant Parasitic nematode in Soybean Crop in Madhya Pradesh Nematode genera Districts Chhindwara Jabalpur Narsingpur Rewa Seoni Satna
Pratylenchus spp 100-2500 50-300 100-600 25-1625 50-900 25-425 578 563 332 410 255 222 (36.84) (51.35) (35.15) (55.55) (62.5) (52.17) Rotylenchulus reniformis 50-900 50-1500 100-625 100-900 100-1000 125-175 390 870 364 310 359 520 (26.31) (13.51) (18.91) (31.27) (33.33) (21.73) Hoplolaimus indicus 25-900 50-650 50-1850 25-250 25-800 25-375 310 290 395 130 260 145 (52.63) (21.62) (43.24) (36.11) (41.66) (32.17) Helicotylenchus spp. 25-2525 25-2000 25-2075 25-1400 100-1175 125-1225 494 797 553 430 431 519 (73.68) (91.89) (89.19) (91.66) (79.06) (100) Tylenchorhynchus spp. 75-75 75-375 50-250 25-100 75-75 75-325 75 200 141 100 75 145 (5.25) (8.10) (16.21) (8.33) (4.00) (21.73) Xiphinema spp. 75-175 525-900 25-400 25-400 25-400 75-400 104 225 169 124 125 154 (31.57) (45.95) (43.24) (66.66) (41.46) (47.82) Aphelenchus avenae 50-550 100-900 200-900 150-600 400-400 150-900 262 375 478 346 400 525 (31.57) (13.51) (21.62) (19.44) (4.04) (8.69) Ditylenchus spp. - 25-125 - - - 25-25 75 25 (5.4) (8.69) Longidorus spp. 50-50 50-50 25-25 - 25-25 - 50 50 25 25 (5.26) (2.70) (5.40) (4.04) Neotylenchus spp. - - 150-175 75-75 225-225 50-50 166 75 225 50 (8.10) (2.77) (4.04) (4.34) Tylenchus & related genera 100-1000 100-2450 50-1050 100-900 125-900 75-400 400 610 392 307 362 211 (47.36) (64.96) (54.50) (55.55) (33.33) (39.13)
148 120 Fig.1 Community structure of PPN in soybean Fig.2.Frequency of Occurrence of PPN in Kymote plateau and satpura Hills in Soybean
Pratylenchus Helicotylenchus spp. Xiphinema spp. Aphelenchus avenae 3500 Tylenchus & related genera R.reniformis Max. Mean 100 Tylenchorhynchus spp.
3000
80
2500
60 2000 Per cent frequency 1500 40 Nematode population Nematode 1000
20
500
0 0 Chhindwara Jabalpur Narsingpur Rewa Seoni Satna Localities TRG
H.indicus A.avanae Xiphinema R.reniformis Ditylenchus Longidorus Pratylenchus Neotylenchus Helicotylenchus Tylenchorhynchus Nematode genera nematode in soybean from Mississippi and Lousiana Tylenchorhynchus spp. (27.73), Xiphinema spp. (66.66), USA. Ditylenchus spp. (16.66), Aphelenchus avenae (31.57), few location contained Neotylenchus spp., Tylenchus and related genera (TRG). The distribution of was The population of reniform nematode, H. indicus found in 56 out of 69 localities. Its frequency of occurrence was recorded from 14 localities Rotylenchulus reniformis was 52.62 per cent in Chhindwara followed by 43.24 per and population was above threshold level (1500 and cent in Narsingpur. Soil Samples from Tendukheda above) of damage with mean nematode population 440 harboured 1850 nematode / 200 cm3 and the crop was /200 cm3. Nematode population was 900, 550, 1500, with stunted growth and chlorosis in patches indicating 1950, 625, 500, 575, 900, 550, and 1000 in Bangao, hot spot of infested pockets. Katangi, Fanwani, Jabalpur, Baghraji, Meregaon, Rithi, H. indicus Parasiya, Bandole and Peepardati localities respectively. The soybean crop grown in aforesaid exhibited stunted References growth with chlorotic foliage and premature flowering. Samples collected from Satna (27.73) and Narsingpur Annon (1985). Biennial Report All India Coordinated Research (21.62) harboured higher frequency of occurrence than Project on Nematode and Their Control. Department other surveyed districts. Widespread occurrence of of Plant Pathology JNKVV Jabalpur 87 p potential migratory ectoparasite, Helicotylenchus spp. Annon (1997). Biennial Report All India Coordinated Research was recorded in soybean crop. The frequency of Project on Nematode and Their Control. Department occurrence was 100, 91.89, 91.66, 89.19, 79.06, and of Plant Pathology, JNKVV Jabalpur pp. 85 73.68 per cent in Satna, Jabalpur, Rewa, Narsingpur, Christie J and Perry VG (1951). Removing nematode from Seoni and Chhindwara respectively (Table 2). The soil. Proc Helminthol Soc Wash 18: 106-108 nematode population in 153 localities ranged between Feldmesser J ( 1971). Estimated crop losses due to plant 25 to 2525 with mean population 1437 nematode / parasitic nematode in the United States Soc Nematol 200cm3 soils, whereas 63 localities harbored moderate Comm Crop losses Supplement 1 : 7 (201 to 500) nematode population. Soybean samples Revios RV and Golden AM (1978). Nematode occurrence in from 19 locations exhibited poor vigour and growth in soybean field in Mississippi and Lousiana Plant Dis terms of stunting and pale colour foliage. In addition to Reptr 62 (5): 433-437 the principal plant parasitic nematodes (Table 1 and Fig Siddiqui M R (1986). Tylenchida (Parasites of Plant and 1), nematode viz., Hoplolaimus indicus (52.62), Insects) Commomwealth Institute of Parasitology 645 p (Manuscript Received: 20.05.2010; Accepted: 18.07.2011)
149 JNKVV Res J 45(2): 150-154 (2011)
Effect of botanicals, oil cakes and bio agents on root-knot (Meloidogyne incognita) management in chilli
S.P. Tiwari, Jayant Bhatt, D.S. Dhakad and Madhuri Mishra Department of Plant Pathology Jawaharlal Nehru Krishi Vishwa Vidyalaya Jabalpur 482004 (MP)
Abstract Material and methods
Dry leaf powder @ 5g/ 500 cm3 soil of ten botanicals, six oil The experiment was conducted by applying ten cakes @ 1 ton/ha and three bioagents @ 0.4g/kg soil, botanicals @ 5g/1.5 kg soil, six bio agents @ 0.4 g/pot improved plant growth and suppressed reproduction of and three oil cakes @ 10 per cent w/w application against Meloidogyne incognita. Among the botanicals, Lantana M. incognita. The nematode population was obtained camera, Azadirachta indica and Jatropha curcas were from Guna district of Madhya Pradesh (Table 1, 2 and 3) effective in reducing root gall formation and enhancing plant and single egg mass culture was maintained in chilli (var. growth significantly. Amendments of soil with neem, jatropha, Pusa Jwala). Steam sterilized soil was mixed with dry and castor cakes @ 4g/plant have satisfactory managed leaf powders of neem, jatropha, linseed, mustard, karanj juveniles of root knot. and Trichoderma viride Paecilomyces and castor @ 10 per cent w/w in one set and bioagents lilacinus were found superior over P. fluorescens in reduction of gall numbers and enhancing the plant growth. viz., Trichoderma viride, Paecilomyces lilacinus and Pseudomonas fluorescens in second set of soil @ 0.4g/ kg soil untreated control for oil cakes and bio agents Keywords: Meloidogyne incognita, glass house, were also maintained. Soil mixed with carbofuran @ 0.4g/ management and chilli kg served as standard check for bio control agents. The soil mixed with cakes and bio control agents was filled Chilli (Capsicum annuum L.) vegetable cum spice is one in ten cm earthen pots individually. Each pot was of the most important commercial crops. It is widely used transplanted with 15 day old healthy seedlings of chilli as spice and named as wonder spice under Indian (var. Pusa Jwala). On establishment of root, the conditions the crop occupied an area of 6.5 lakh ha. with seedlings were inoculated with second stage juveniles of @ 2 J2 /g soil with replicated five times. 10.64 lakh tons production during 2005-06 (Anon 2005). M. incognita Madhya Pradesh alone contributes an area of 46,658 The pots were randomized on glass house bench ha with 42,459 production and 0.91 tons /ha productivity. following complete randomized design (CRD). Pots were irrigated with fresh tap water as and when required. Root knot nematode (Meloidogyne spp.) has been Adequate plant protection measures were adopted to a major threat in almost all the cultivated area in tropical grow healthy crop. and sub tropical regions. More than 150 species of Meloidogyne are reported that remained associated with 2000 plant species and cause approximately five per Results and Discussion cent of global losses. During July, 2005 to Sept. 2006 an intensive survey was carried out in all the agro climatic Botanicals on nematode management regions of Madhya Pradesh and root knot nematode was identified as predominant nematode pest associated with Dry leaf powders of Azadirachta indica, Calotropis chilli. The roots of chilli showed severe galling, stunted procera Datura metel, Hibiscus rosasinensis, Ipomoea growth and yellowing of younger foliage due to the carnea, Jatropha curcas, Lantana camera, Pongamia infection of Meloidogyne incognita (Kofoid and White pinnata, Ocimum sanctum and Parthenium Chitwood 1949). hysterophorus were found significantly effective towards
150 Table 1. Efficacy of dry leaf powders of some plants against M. incognita
Treatments Common names Growth Parameters Population Shoot Fresh Dry shoot Fresh No. of No. of egg Soil Root height shoot shoot root gall/pl masses/ (200cc) (5g) (cm) wt. (g) wt. (g) wt. (g) plant
Calotropis procera Akaua 18.30 3.82 1.88 2.28 27.60 24.00 616 614 @ 5g/1.5kg soil (24.74) (24.71) Azadirachta indica Neem 7.06 0.84 0.26 0.68 6.80 6.20 786 340 @ 5g/1.5kg soil (27.28) (17.76) Parthenium histerophorus Parthenium 20.40 4.14 2.50 2.74 14.80 13.00 201 162 @ 5g/1.5kg soil (13.83) (12.60) Jatropha curcas Jatropha 9.48 0.76 0.22 0.36 8.80 8.20 141 560 @ 5g/1.5kg soil (11.71 (23.27) Datura metel @ 5g/ Datura 19.92 5.24 2.94 3.16 15.60 13.40 88.8 86.6 1.5kg soil (9.29) (7.44) Ipomea carneea @ 5g/ Ipomea 14.62 2.90 0.88 1.78 48.20 45.80 88 203 1.5kg soil (9.30) (14.17) Pongamia pinnata Karanj 21.60 4.18 3.14 2.34 68.20 63.40 167 256 @ 5g/1.5kg soil (12.73) (15.42) Lantana camera @ 5g/ Lantana 4.54 0.42 0.12 0.12 5.40 4.40 92 104 1.5kg soil (9.46) (10.14) Hibiscus rosasinensis Gudhal 24.16 4.62 2.90 3.24 32.00 29.20 223 290 @ 5g/1.5kg soil (14.15) (15.58) Ocimum sanctum Tulsi 20.12 4.76 1.72 2.88 27.40 24.60 203 203 @ 5g/1.5kg soil (12.39) (13.93) Control 18.30 4.64 2.90 6.06 81.20 75.20 853 930 (28.13) (30.37) SEm± 2.30 0.97 0.61 0.83 5.89 5.42 2.03 1.50 CD ( P= 0.05) 4.63 1.96 1.23 1.68 11.87 10.92 3.97 2.94
Table 2. Effect of oil cakes on reproduction of M. incognita and plant growth
Treatments Growth Parameters Population Shoot Fresh Dry shoot Fresh No. of No. of egg Soil Root height shoot shoot root gall/pl masses/ (200cc) (5g) (cm) wt. (g) wt. (g) wt. (g) plant Castor @ 10 % w/w 16.92 3.18 0.40 1.66 21.60 17.60 320 174 (17.85) (13.14) Karanj @ 10 % w/w 14.88 2.08 0.28 1.18 12.00 9.20 252 205 (15.27) (12.81) Mustard @ 10 % w/w 19.70 5.76 1.10 3.08 45.20 40.20 512 702 (22.58) (24.13) Linseed @ 10 % w/w 24.90 6.32 2.12 4.58 48.80 44.2 591 634 (23.97) (24.43) Jatropha @ 10 % w/w 24.84 5.98 2.30 4.16 56.20 52.2 751 614 (26.73) (24.71) Neem @ 10 % w/w 19.56 4.60 1.36 3.04 8.00 7.00 151 114 (11.83) (9.62) Control 18.30 4.64 2.90 3.06 81.20 75.20 616 846 SEm± 3.91 0.83 0.85 0.76 0.69 0.67 2.57 4.38 CD ( P= 0.05) 8.01 1.70 1.75 1.57 1.42 1.38 5.26 8.98
151 Table 3. Efficacy of bio-agents on multiplication of root knot Treatments Growth Parameters Population Shoot Fresh Dry shoot Fresh No. of No. of egg Soil Root height shoot shoot root gall/pl masses/ (200cc) (5g) (cm) wt. (g) wt. (g) wt. (g) plant Trichoderma viride @ 0.4g/kg soil 13.78 2.40 1.40 1.02 53.00 25.40 360 216.4 (19.18) (14.54) Paecilomyces lilacinus @ 0.4g/kg soil 14.80 2.54 1.48 1.50 58.60 49.40 372 331 (16.50) (18.11) Pesudomonas fluorescens @ 0.4g/kg soil 14.98 1.88 1.22 1.26 59.80 39.40 402 242 (20.05) (15.44) Carbofuran @ 0.4g/kg soil 14.92 1.94 1.08 0.94 59.60 50.00 371 386 (19.22) (19.60) Control 14.40 1.50 0.46 1.22 85.00 78.20 462.6 573.8 (21.43) (23.74) SEm± 1.21 0.32 0.19 0.27 10.32 8.53 1.98 1.59 CD ( P= 0.05) 2.53 0.67 0.39 0.57 21.53 17.80 4.13 3.32
90 1000 reduction of root knot population as compared to 80 900 70 800 untreated control. 60 700 50 600 500 The total number of root galls were 5.40, 6.80 and 40 400 30 8.80 per plant in Lantana camera, Azadirachta indica and
300 Populations 20 No.ofgalls/ eggs 200 Jatropha curcas, respectively. The significant reduction 10 100 0 0 in egg masses was noticed in all the treatments except Pongamia pinnata (63.40). However, least (4.40) egg Control A. indica J. curcas D. metelI. carneea L. camera masses development was recorded in C. procera P. pinnata O. sanctum Lantana camera. H. rosasinensis P. histerophorus Treatments Population of M. incognita in soil and roots of all the No. of eggs No. of galls population in soil Population in root treatments were observed in the range of 88 to 786 in Fig. 1: Efficacy of dry leaf powders of some plants against soil, whereas juveniles were 86.6 to 614 / 5g root. The M. incognita least soil population was observed in the Datura metel (88.8/200cm3) whereas in root the number of juveniles 100 80 were 86.6/5g root followed by Lantana camera (92/200 70 3 3 80 cm and 104/5g root) and Ipomoea carnea (88.8/200 cm 60 and 203/ 5g root). All the treatments were statistically 50 60 No. of galls 40 significant over control. Plant height was ranged from 40 30 No. of eggs 4.54 to 24.16 cm. Maximum (24.16 cm) plant height was 20 20 observed in Hibiscus rosasinensis followed by
No. of galls / eggs / galls of No. 10 0 0 Parthenium hysterophorus (20.40) and Ocimum sanctum Castor Karanj Mustar Linseed Jatropha Neem Control (20.12). The fresh shoot weight was evidenced that D. exhibited maximum (5.24 g) shoot weight followed Treatments metel by Ocimum sanctum (4.76) and Hibiscus rosasinensis Fig. 2. Effect of Oil cakes on reproduction of M. incognita (4.62). Maximum (3.24) fresh root weight was recorded and plant growth in Hibiscus rosasinensis, whereas Datura metel (3.16) 90.0 was next in order of fresh root weight. However, other 80.0 70.0 treatments were also found superior over control (Table 60.0 50.0 No. of galls 1 and Fig.1). 40.0 No. of eggs 30.0 20.0 10.0
No. of galls / eggs Efficacy of oil cakes on root knot reproduction in chilli 0.0
T. viride Co ntrol Organic amendments with cakes viz., Castor, karanj, P. lilacinus Carbofuran P. fluorescens mustard, jatropha, linseed and neem showed significantly Tre atments reduction of M. incognita and improved the growth parameters (Table 2 and Fig. 2). The shoot height of chilli Fig. 3. Efficacy of bio-agents on multiplication of root knot
152 was significantly increased due to incorporation of viride followed by P. lilacinus (242) while minimum organic cakes. Performance of linseed cake was reduction (386) was recorded in carbofuran. Application significantly superior (24.90 cm) over control while of different botanicals, leaf powders and bio agents minimum 14.88 cm was recorded in karanj cake. The significantly reduced the population of M. incognita and fresh shoot and root weights of chilli were maximum (6.32 improved the shoot height (cm), fresh and dry weight of and 4.58) in linseed whereas karanj cake showed shoot, fresh weight of root, number of galls, egg masses. minimum (2.08 and 1.18) shoot and root weight. The data on dry weight basis indicated that jatropha was Among the botanicals, Lantana camera, found most effective over control and rest of the Azadirachta indica and Jatropha curcus significantly treatments had maximum (2.30 g) dry weight. The reduced development of galls and improved the growth significant reduction in both number of galls/root system parameters of chilli. Incorporation of neem, jatropha, and and egg masses were observed in all the treatments castor cakes @ 4g/plant have satisfactory managed root except in jatropha where more galls and eggs were 56.2 knot nematode. Bio agents T. viride and P. lilacinus were and 52.2 respectively. found superior over P. fluorescens and untreated control towards reduction of galls and enhancing the plant Chilli plant treated with neem cake was best growth. Earlier several studies have been conducted among the treatments in terms of reducing galls (8) and and proved efficacies of botanicals, oil cakes and bio egg masses (7) over control. Although, all the treatments agents in suppressing the M. incognita (Prasad et al were significantly superior in reducing soil and root 1964; Goswami and Vijayalakshmi 1986; Pathak and population of M. incognita. Neem cake was very effective Saikia 1999; Sankaranarayanan et al 1999; Vadhera et towards reducing nematode population in soil (151) and al 2000; Ahmed and Choudhary 2004; Khan and Verma root (114) followed by karanj (252) in soil and 205 root. 2004; Patel and Patel 2007; Sartaj et al 2007).
Efficacy of bio-agents on multiplication of root knot nl okuLifrd ikS/ks ¼5 xzke@500 ?ku ls-eh-½ 'kq"d pw.kZ] N [kyh ¼4 g@ikS/ks½ rFkk rhu tSfod fu;a=d ¼0.4 xzke@fdyks e`nk½ dh nj ls Significant differences in shoot height in different esykbMksxk;uh budkxfuVk ds iztUku dks 'keu djrs gS rFkk ikS/kksa esa treatments. The data on shoot height indicated that treatments with Pseudomonas fluorescence was mUufr djrs ik;s x;s gSaaA ySUVkuk desjk] v>kfnjDVk bafMdk rFkk superior (14.98) followed by Carbofuran (14.92) and tVªksQk djdl ds okuLifrd pw.kZ tM+ xzafFk ds lk/kd ds vfrjfDr Paecilomyces lilacinus (14.80) respectively (Table 3 and fePkZ ds ikS/kksa dh mUufr djrs ns[ks x;sA uhe] tVªksQk rFkk vj.M Fig. 3). dh [kyh ls tM+ xzafFk ds uotkr dk 'keu dks ns[kk x;kA VªkbdksMjek Soil treatment with Trichoderma viride and fofjMh rFkk filhyksekblhl yhykflul tSfod fu;a=d lqMkseksukl Paecilomyces lilacinus were statistically at par with each other in respect to the fresh shoot weight (1.94 and 2.54 ¶yksjslsUl dh vis{kk tM+ xzfUFk dh la[;k ds 'keu ds vfrfjDRk ikS/ g). Similar observation with regard to dry shoot weight kks dh mUufr esa fof’k"B ik;s x;sA was also noted in Paecilomyces lilacinus treated pots. Maximum (1.48 g) dry shoot weight was noted with P. Reference lilacinus but was at par with Trichoderma viride (1.40 g). Significant and maximum fresh root weight (1.50 g) was observed in Paecilomyces lilacinus while minimum (0.94 Ahmed JA, Choudhary BN (2004) Management of root knot g) was recorded in Carbofuran. nematode Meloidogyne incognita using organic amendments in frenchbean. Indian J Nematol 34: The number of galls/ plant was significantly less 137-139 in Trichoderma viride. Rest of the treatments exhibited Goswami BK, Vijayalakshmi (1986) Effect of some indigenous reduced galling ranged from 58.60 to 59.80. Significant plant material and oilcake amended soil on the reduction in egg masses/ root system was observed in growth of tomato and root - knot nematode all the treatments over control. Treatment Trichoderma population. Ann Agric Res7: 360-366 viride was found best (25.40) among all the treatments. Khan MN, Verma AC (2004) Biological control of Meloidogyne Maximum number of eggmasses was obtained in incognita in pointed gourd. Deptt Nematology ND Univ Agri & Tech Kumarganj pp 224-229 carbofuran (50.00) followed by P. lilacinus (49.40). The Population of M. incognita was inhibited by T. viride (360 Patel BA, Patel SK (2007) Efficacy of Jatropha formulation N) followed by carbofuran (371) and (372). against root knot nematode in tomato nursery and P. lilacinus field condition, National Symposium on Nematology The lowest root population (216.40) was recorded in T.
153 in 21st Century: Emerging Paradigms (Nov 22-23) sunflower. Proc. National Symposium on Rational pp 33 Approaches in Nematode management for Prasad SK, Dasgupta DR, Mukopadhaya MC (1964) Sustainable on Rational Agriculture GAU Anand India Nematode associated with commercial crops in 23-25 Nov pp 25-27 Northern India and host range of Meloidogyne Tiyagi A, Mahmood I, Azam MF (2007) Biological control of javanica (Treub 1885) Chitwood 1949. Indian J Ent soil pathogenic nematode infecting chickpea using 26: 438-446 Pseudomonas fluorescens. National Symposium on Pathak JJ, Saikia B (1999) Biological control of root knot Nematology in 21st Century: Emerging Paradigms. nematode of betelvine. J Inter Academicia 3: 117- Assam Agricultural University Jorhat 22-23 Nov pp 120 65 Sankaranarayanan C, Hussaini SS, Kumar PS, Vadhera I, Tiwari SP and Shukla BN(2000) Field evaluation of chemical management of root knot nematode in Rangeshwaran R, Kaushal KK (1999) Antagonistic (Manuscript Received: 20.05.2011; Accepted: 15.07.2011) effect of Trichoderma and Gliocladium spp. against tomato. Indian Phytopath 53: 32-34 root knot nematode, Meloidogyne incognita on
154 JNKVV Res J 45(2): 155-158 (2011)
Pathogenic potential of root knot nematode (Meloidogyne incognita) on lentil and its effects on plant age
Jayant Bhatt, S.P. Tiwari and Kamlesh Pawar Department of Plant Pathology Jawaharlal Nehru Krishi Vishwa Vidyalaya Jabalpur 482004 (MP)
Abstract incognita and subsequent influence on lentil plant age.
An experiment, to determine pathogenic potential of root knot Materials and methods nematode (Meloidogyne incognita) on lentil and its influence on plant age, was conducted in pots underglass house conditions. All the plant growth parameters were declined at Confirmation of pathogenic potential of root-knot nematode on lentil 1000 J2/pot where as at highest inoculum level (10,000 J2/ pot) the plant became stunted with significant reduction in growth parameters. The inoculum threshold level of M. Bold and healthy seeds of Lentil (cv. JL3) were sown in on lentil was recorded 2J /g soil. All the stages of incognita 2 10 cm diameter earthen pots holding 500 cm3 steam lentil were penetrated by root knot nematode. Lowest level of sterilized soil. In each pot, two seeds were sown at an inoculum at the initial stages of crop can result in substantial even depth of two cm. After emergence single seedling damage to the crop. was retained. Seven days after when seedlings attained a height of eight cm the pots were inoculated with series Key words: Meloidogyne incognita, Lentil, Pathogenicty, of inoculum levels of root-knot nematode. The second Plant age stage juveniles of M. incognita were obtained from the culture previously maintained on lentil plants. Serial dilutions were prepared to obtain levels of desired inocula Lentil ( L.) is an important pulse crop of Lens culinaris in logarithmic series viz. 0, 10, 100, 1,000 and 10,000 State and is sown under varied situations and soils, in larvae that were dispersed individually in 10 ml of sterile all parts of country either as sole or mixed crop. It is also water. In control pots ten ml of sterile water alone (with planted after rice as other crop in some regions. In no nematode population) was applied. Madhya Pradesh lentil is cultivated in light as well as black heavy soils. Lentil occupies nearly 1.51 m ha in Inoculation of lentil seedlings with desired level of country with 0.95 m tonnes production and 629 kg inoculum were carried out by removing the soil from the productivity. Madhya Pradesh occupies 0.582 m ha area rhizosphere in radius of 2.0-2.5 cm and dispersing the with 0.293 m tonnes production and 503 kg productivity required populations by dispenser holding appropriate which is nearly 30.80 percent of the total production in populations in ten ml water. After inoculation, the roots the country,(Anon 2006) were covered with a thin layer of fresh steam sterilized soil. Each treatment was replicated five times and The root-knot nematode, Meloidogyne incognita randomized on glass house bench. These pots were (Kofoid and White,1919) Chitwood 1949, has been irrigated with 100 ml of fresh tap water as per encountered in some of the lentil growing areas that plays requirement. The glass house temperature ranged as one of the limiting factors affecting crop growth and between 28-320C during the course of investigation. The production. Yield losses due to the nematode was experiment was terminated at 45 day after inoculation. estimated to 5 percent (Haider et al. 2003). The present investigations ware undertaken under glass-house Effect of root knot nematode on plant age conditions to confirm the pathogenic potential of M.
155 The experiment was conducted in 10 cm earthen pots There was reduction in fresh shoot weight of lentil containing 500 cm3 sterilized soil. The lentil seedlings of plants with increase in inoculum level. Significantly three different ages viz. 7(S1), 14(S2) and 21(S3) days reduced shoot weight (0.31 g) was noted with 10,000 J2/ were chosen for the experiment. Each treatment was plant followed by 1000 J2 (0.41 g) and 100 J2/plant (0.43 replicated five times and randomized. g). Although the fresh shoot weight in control (0.65 g)
and 10 J2/plant (0.49 g) were statistically at par but reduction was also noted in this treatment on fresh weight Results and Discussion basis.
Confirmation of pathogenic potential of root-knot On dry weight basis, maximum (0.33 g) shoot nematode on lentil. weight was recorded with control which was superior over all the treatments. Minimum (0.16 g) shoot weight
was recorded with 10,000 J2/plant followed by 1,000 J2/ There was a gradual stunting of plants when inoculated plant (0.17 g). Reduced dry shoot weights were also with Table 1) In the treatment where highest M. incognita.( recorded in 10 J2/plant (0.24 g) and 100 J2/plant (0.20 population (10,000 J2/plant) of the nematode was added g). plants exhibited chlorosis and defoliation. The plant looked sick and devitalized with marked retarded growth. The fresh root weight of the lentil plant was The plant height in the treatment was noted to be 11.60 adversely affected by the nematode infection. Minimum (0.22 g) root weight was recorded with 10,000 J /plant cm. There was a gradual reduction in plant height as 2 followed by 1000 J /plant (0.34 g). Reduced root weights the inoculum level increased. Maximum (19.20 cm) plant 2 were also recorded in 10 and 100 J /plant which were height was recorded with control which was superior over 2 all other treatments. Minimum (11.60 cm) plant height recorded to be 0.39 and 0.33 g respectively. Maximum was observed in the treatment where 10,000 nematodes fresh root weight was recorded with control (0.49 g). were incorporated with the soil followed by 1,000 J /plant 2 Least (8.6) root galls were noticed with minimum where the height was recorded to be 16.20 cm. inoculum level (10J2/plant) and increased with an There was a gradual decrease in root length as increase in the inoculum level. The number of root galls was maximum (60.6) in 10000 J /plant followed by 1,000 the inoculum level increased. Maximum (12.00 cm) root 2 J /plant (51.8). Only 22.4 galls were recorded in the length was noted in control, which was superior over all 2 treatment of 100 J /plant were incorporated. other treatments (Table 1). Minimum (7.40 cm) root length 2 was noted with 10,000 J /plant followed by 1000 J /plant 2 2 Maximum (59.2) egg masses per plant were (9.20 cm) and 10.80 cm in 100 J /plant. Reduction in 2 recorded with 10,000 J followed by 1,000 J /plant (53.2). the root length (11.60 cm) was also noted in the minimum 2 2 Minimum (8.2) egg masses were recorded in 10J2/ inoculum level. (10 J2/plant) followed by 100J2/ plant (20.2) against no egg masses
Table 1. Influence of different levels of inocula on growth parameters of lentil and reproduction of M. incognita
Inoculum level Shoot ht. Root Shoot wt. (g) Root wt. No. of galls No. of egg Nematode population (cm) length (g) masses (cm) Fresh Dry Soil Root Total Control 19.20* 12.00 0.65 0.33 0.49 0.0 0.0 0.0 0.0 0.0 (0.71)** (0.71) (0.71) (0.71) (0.71) 10 18.40 11.60 0.49 0.24 0.39 8.6 8.2 47.0 45.4 92.4 (3.00) (2.93) (6.80) (6.72) (9.58) 100 19.00 10.80 0.43 0.20 0.33 22.4 20.2 198.0 160.6 358.6 (4.73) (4.45) (13.69) (12.36) (18.52) 1000 16.20 9.20 0.41 0.17 0.34 51.8 53.2 582.0 474.0 1056.0 (7.20) (7.32) (24.06) (21.74) (32.49) 10000 11.60 7.40 0.31 0.16 0.22 60.6 59.2 812.0 556.0 1368.0 (7.80) (7.71) (28.37) (23.51) (36.88) SEm ± 1.39 0.98 0.04 0.03 0.04 0.26 0.28 1.09 0.86 1.25 CD (P=0.05) 4.11 2.89 0.13 0.09 0.12 0.77 0.81 3.20 2.54 3.38 * Mean of five replications ** Figure in parentheses are square root transformed values
156 Fig.1 Influence of different levels of inocula of M.incognita All the stages (S1, S2 and S3) of the plant growth
70 900 were susceptible to root-knot nematode. The plants under S and S receiving maximum levels of inoculum 800 1 2 60 were collapsed within 20 days of inoculation hence the 700
50 data on plant growth parameters could not be recorded. 600
40 500 Galls/plant The plant height was significantly reduced at all Eggmasses/galls Soil/200cc 400 the levels of inoculum and stages of plant growth except 30 Root/5g Gaall/eggmasses
Emergence of juveniles in the highest level of inoculum at S and S where the 300 1 2 20 plants died due to heavy infection of root-knot. Maximum 200 height was recorded in control for S (18.0 cm), S (19.00 10 1 2 100 cm) and S3 (21.80 cm) stages and least in S3 (14.60 0 0 0 10 100 1000 10000 cm) at maximum inoculum level. There was a gradual Levels of inocula reduction in plant height for all the stages as the inoculum in control. level increased. A decline was recorded in the plants inoculated with 1000 J2/plant for all the stages which Minimum (47.0) nematode population in soil was was lower than control and rest of the treatments.(Table recorded with minimum inoculum level of 10 J2/plant and 2) increased with an increase in inoculum level. The numbers of nematode were maximum (812.0) in 10,000 Maximum root length was recorded with uninoculated control and minimum with 10,000 J2 in S1 J2/plant followed by 1000 J2/plant (582.0). One hundred ninety eight nematodes were recorded in 100 J2/plant. (8.60 cm). Reduction in root length was observed in all the stages of plant growth inoculated with 1,000 J2. The Maximum (556.0) nematodes in plant root were root lengths in this treatment ware 10.80 and 11.40 cm recorded in 10,000 J /plant followed by 1,000 J /plant 2 2 for S2 and S3, respectively. There was a gradual decline (474.0). Minimum (45.4) nematodes were recorded in in shoot weight (fresh) in all the stages of the plant growth
10 J2/plant followed by 100 J2/plant (160.6) against no as the levels of inoculum increased. A decline in fresh nematodes in control. shoot weight was noticed in 1,000 J2 level of inocula at all the stages. Maximum (0.61 g) fresh shoot weight was Similar findings were brought forward by Birat recorded in uninoculated (control) for all the stages which (1968) in case of okra with and Reddy (1975) M. incognita was significantly superior over rest of the treatments in chickpea with . Inhibitory effects of root M. javanica followed by 10 and 100 J levels which were at par with knot nematode on chickpea are reported ( Dhawan and 2 each other. Sethi 1976; Gaur and Prasad 1980 Mani and Sethi 1984).
They reported that an incoculum level of 2J2/g soil was On dry weight basis, maximum (0.29 g) shoot found to be damaging threshold. In present investigations weight was recorded in un inoculated (control) which was 1000 nematodes / 500 g soil were found to be pathogenic superior over rest of the treatments for all the stages. on lentil (cvJL 3). 1,000 J2 level showed sharp decline when compared to
100 J2 inoculum level followed by 10 and 100 J2 levels for all the three stages of plant growth. Minimum (0.15 Effect of root knot nematode on plant age
Table 2. Effect of plant age and levels of inocula of M. incognita on lentil plant growth and nematode multiplication
Inoculum l Shoot ht. (cm) Root length (cm) Shoot wt. (g) Root wt. (g) level Fresh Dry
S1 S2 S3 S1 S2 S3 S1 S2 S3 S1 S2 S3 S1 S2 S3 0 18.00* 19.00 21.80 10.40 12.40 12.40 0.42 0.46 0.61 0.19 0.23 0.29 0.48 0.44 0.62 10 15.20 16.60 17.00 11.40 12.40 16.40 0.43 0.48 0.62 0.24 0.22 0.35 0.43 0.40 0.47 100 13.60 16.00 15.80 9.60 11.40 11.00 0.38 0.40 0.51 0.24 0.19 0.24 0.38 0.38 0.45 1000 11.80 14.60 17.20 8.60 10.80 11.40 0.32 0.36 0.51 0.15 0.17 0.26 0.32 0.34 0.49 10000 0 0 14.60 0 0 10.60 0 0 0.34 0 0 0.17 0 0 0.41 SEm ± 1.03 1.15 1.55 0.90 1.06 1.17 0.04 0.04 0.04 0.03 0.02 0.03 0.03 0.04 0.05 CD (P=0.05) 3.05 3.39 4.59 2.65 3.13 3.45 0.12 0.12 0.12 0.08 0.06 0.09 0.10 0.13 0.15
*S1 = 7 days old seedlings S2 = 14 days old seedlings S3 = 21 days old seedlings
157 g) shoot weight was recorded in S in 1,000 J level. 1 2 kksa esa ckSukiu ik;k x;k A elwj ij tM+xzfFk lw=—fe dh jksx tudrk Minimum (0.32 g) root weight was recorded in the 2 fMaEHkd@xzke e`nk vkadh xbZA elwj ds ikS/ks dh lHkh voLFkk;sa tM+ plants inoculated with 1000 J2 inoculum level S1. xzafFk lw=—fe ds izHksnu gsrq vuqdwy ikbZ xbZA ikS/ks dh vkjafHkd Maximum (0.62 g) root weight was recorded for voLFkk esa lw=—fe dh fuEu la[;k ds }kjk gksus okys ladze.k ds uninoculated S which was significantly superior over rest 3 dkj.k elwj dh Qly ij dqizHkko ns[kk x;kA of the treatments. The root weight in 1,000 J2 level had drastically declined at S1 (0.32 g) followed by 0.24 and 0.49 in S and S respectively. 2 3 References
The number of galls increased in 1,000 J2 level Anonymous (2006) Agriculture Statistics 2001 Department of which was recorded to be 30.8 in S1, 41.0 in S2 and 33.4 Agriculture M P State Agricultural Marketing Board in S3. The gall numbers were again declined in the pp 112 survived plants of S3 (52.2) inoculated with 10,000 J2. Birat R B S (1968) Inoculation traits with Meloidogyne javanica The numbers of egg masses/plant were gradually and on okra (Abelmoschus esculentus). Nematologica significantly increased with increase in inoculum level of 14:155-156
1,000 J2 for all the stages. At this level maximum number Dhawan S C, Sethi C L (1976) Observations on the of egg masses/plants was recorded with S2 (39.8) pathogenicity of Meloidogyne incognita to eggplant and on relative susceptibility of some varieties to the followed by S1 (35.0) and S3 (30.6). Similar trend was nematode. Indian J Nematol 6:39-46 also observed for 100 J2 level. Minimum 13.4 egg Eapen S J (1992) Influence of plant age on root-knot nematode masses were recorded at S3 and Maximum (20.0) at S2 followed by 15.0 at S stages. This trend was again development in Cardomom. Nematol Medit 20:2 193- 1 195 observed at 10 J2 levels. Gaur H S , Prasad S K (1980) Population studies on Minimum (49.0) nematode population in soil was Meloidogyne incognita on eggplant (Solanum recorded in S2 with minimum inoculum level of 10 J2/ melongena) and its effect on the host. Indian J plant and increased with increase in inoculum level. The Nematol 10:40-52 Gupta D C, Verma K K (1990) Studies on available losses in numbers of nematode were maximum in S3 (1264.0) in mung bean due to root and its control under field 10,000 J2/plant. As such no nematode population was recorded in control. condition. Indian J Nematol 20:148-151 Haider M G, Dev L K, Nath R P (2003) Comparative The number of nematode increased in 1,000 J2 pathogenicity of root-knot nematode (Meloidogyne level, which was recorded to be 866.0 in S3, 870.0 in S2 incognita) on different pulse crops. Indian J Nematol 33:152-155 and 900.0 in S1. The nematodes in root/plant were again declined in the survived plants of S3 1324.0 inoculated Jadhav PV, Kurundkar B P (1991) Effect of age of okra plants with 10,000 nematodes/plant. on multiplication of Meloidogyne incognita. Indian Phytopath 44:504-506 The root-knot nematode continues to penetrate Kalita D N, Phukan (1993) Pathogenicity of Meloidogyne the roots at all the stages of plant growth. Even lowest incognita on black gram. Indian J Nematol 23:105- level of inoculum at initial stages of the crop can result 109 substantial damage at the later stage of the crop. These Mani A, Sethi C L (1984) Plant growth of chickpea as results confirm the findings of Pandey (1984) on mung influenced by initial inoculum levels of Meloidogyne bean; Jadhav and Kurundkar (1991) on okra and Eapen incognita. Indian J Nematol 14:41-44 (1992) on cardamom who reported that young seedlings Panday S. K. (1984) Studies on root-kont of green gram (Vigna were more susceptible to root-knot than mature plants. radiata L. Wiezeck) M Sc Thesis Department of Plant Pathology JNKVV Jabalpur pp 49 Reddy P P, Nagesh M, Devappa V, Verghese A (1998) tM+ xzafFk lw=—fe ¼ehyk;Mksxk;uh budkxuhVk½ dh jksx tudrk dk Management of Meloidogyne incognita. Indian J elwj ij izHkko Kkr djus ds fy;s ,d iz;ksx xeyksa esa dkap?kj Nematol 5:200-2006 okrkoj.k esa fd;k x;kA ikS/ks ds leLr o`f) xq.kksa esa 1000 fMEHkd@xeyk Sharma G L, Baheti B L (1992) Loss estimation due to root- (Manuscriptknot Received: nematode 29.05.2011; in peas, Accepted: okra, tomato,30.06.2011) and bottle ds ladze.k ds dkj.k deh ikbZ xbZ] tcfd 10000 fMEed ij ikS/ gourd crops in Rajasthan Indian Curr Nematol 3:187- 188
158 JNKVV Res J 45(2): 159-161 (2011)
Tolerence of Trichoderma viride to fungicides under artificial conditions
Ranavay Kumar, J.P. Upadhyay and Sanjeev Kumar* Department of Plant Pathology Rajendra Agricultural University (Campus Dholi) Pusa 484121 (Bihar) *Department of Plant Pathology College of Agriculture, Jawaharlal Nehru Krishi Vishwa Vidyalaya Jabalpur 482004 (MP)
Abstract study was conducted to evaluate the tolerance of Trichoderma viride with commonly used fungicides, Nine fungicides namely carbendazim, hexaconazole, which could in future be recommended as possible thiophanatemethyl, copper oxychloride, mancozeb, propineb, combination in integrated disease management program carbendazim + mancozeb, metalaxyl +mancozeb and me. iprodione + carbendazim were evaluated at lower concentration than the recommended dose to study the tolerance by T. viride. Copper oxychloride was least toxic Material and methods followed by propineb to the growth and sporulation. Study indicate that these fungicides may be applied safely as seed The Investigation were conducted in Department of Plant treatment along with bioagent T. viride. Carbendazim, alone and in mixture proved highly toxic and not advocated for Pathology Tirhut College of Agriculture, Dholi, seed or soil application along with T viride. Muzaffarpur (RAU Pusa, Bihar) during the year 2006- 07. Commercial formulation of nine fungicides were evaluated to observe the sensitivity against T. viride. The Keywords: Fungicides, Tolerance, poison food technique fungicides selected carbendazim (Bavistin), hexaconazole (Contaf), thiophanate methyl (Topmost ) Use of chemicals for the control of plant diseases has of systemic group, mancozeb ( Indofil- M 45 ), copper debatable because of the costs involved and related oxychloride ( Blitox 50), propineb (Antracol) of protective adverse effect on environment (Madhavi et al. 2008). group and carbendazim + mancozeb (Companion), The focus on the management of plant diseases has metalaxyl + mancozeb (Matco 8- 64) and iprodione + been shifted from chemical pesticides to biocontrol to carbendazim (Quintal ) of mixture of systemic and reduce environmental hazards and to minimize the risk protective fungicides were evaluated for growth inhibition of development of pesticides tolerance strains of plant of T. viride by poisoned food technique on potato pathogens. Trichoderma spp had been established as dextrose agar medium ( Nene and Thapliyal 1993). potential biocontrol agents during past few decades have Potato dextrose agar medium poisoned with different created a new milestone in non-chemical plant disease fungicides were poured into sterilized petridishes @ 20 management system and organic farming (Pan and ml / plate and allowed to solidify. After solidification 5 Bhagat 2008). Integration of pesticides and biocontrol mm culture disc of T. viride cut from 3 days old culture agents has been the subject of research over the years. were placed in the center of poured plates. Potato Integration of biological and chemical control methods dextrose agar medium not amended with any fungicide has the potential to control plant pathogens with minimal but inoculated with test fungus served as control. The interference in the natural biological equilibrium treatments were replicated thrice. All the plates were (Papavizas 1985). Biological approach can be successful incubated at 28 ±10C. Observations on colony diameter if the biocontrol agents are compatible with fungicides were recorded after 48 hours and 72 hours of incubation. and biopesticides. (Papavizas 1985). Hence, the present Percent inhibition of growth over check was calculated
159 by using the formula described by Vincent (1947 ). All the nine fungicides were evaluated at lower Sporulation was recorded after 72 h of incubation. To concentrations than the recommended dose to study the count the number of spores of T. viride per ml of tolerance by T. viride. Carbendazim, hexaconazole, suspension, 5 mm disc T. viride was cut with sterilized carbendazim + mancozeb and iprodione + carbendazim cork borer from each replication , washed with camel at all concentrations completely inhibited the growth of brush in 2 ml of water and again in 3 ml of water. Finally T. viride. (Table- 1). Copper oxychloride was least toxic 5 ml fungal spore suspension was obtained. Ten to T. viride as it inhibited the growth of T. viride to a observations were taken from each replication and extent of 2.5, 14.8 and 22 .6 percent at 200, 500 and number of spore / ml was counted using 1000 µg/ml, respectively. The results obtained are in haemocytometer. Per cent data were transformed in aggrement with finding of Karpa gavalli ( 1997) who stated Angular transformation for statistical analysis. The data that copper oxychloride was least inhibitory to radial were statistically analysed using Completely Randomized growth of T. harzianum and T. viride. Thiophanate methyl, Design. mancozeb and mixture of metalaxyl + mancozeb showed 27.1 to 73. 9 percent inhibition of growth of T. viride. Pandey and Upadhyay ( 1998 ) also reported that T. viride Results and Discussion and T. harzianum were highly sensitive to carbendazim. Desai and Kulkarni (2004) reported that carbendazim,
Table 1. Effect of fungicides on growth and sporulation of T. viride Fungicides Concentrations Colony dia. Growth inhibition No. of spores/ml Inhibition of (µg/ml) (mm)*after 72hr over check ( %) ( x 106) spore production 72 hrs 72 hrs over check Systemic Carbendazim (Bavistin) 25 0.00 100( 90) 0.00 0.00 50 0.00 100( 90) 0.00 0.00 100 0.00 100( 90) 0.00 0.00 Hexaconazole (Contaf) 25 0.00 100( 90) 0.00 0.00 50 0.00 100( 90) 0.00 0.00 100 0.00 100( 90) 0.00 0.00 Thiophanatemethyl (Topmost) 25 40.67 54.67 ( 47.67 ) 9.47 20.21 50 34.67 61.34 ( 51.56 ) 8.47 28.64 100 23.33 73.98 ( 59.34) 6.93 41.61 Non systemic Copper oxychloride (Blitox- 50) 200 87.33 2.58 ( 9.02 ) 9.53 19.71 500 76.33 14.86 ( 22.66 ) 7.47 37.06 1000 69.33 22.67 ( 28.43 ) 5.47 53.91 Mancozeb (Indofil M- 45) 200 57.67 35.68 ( 36.68 ) 4.53 61.83 500 47.00 47.58 ( 43.61 ) 3.47 70.76 1000 36.67 59.11 ( 50.25 ) 2.27 80.87 Propineb (Antrocol) 200 67.67 24.53 ( 29.69 ) 8.07 32.01 500 58.67 34.57 ( 36.01 ) 4.93 58.46 1000 52.33 41.63 ( 40.18 ) 3.07 74.13 Systemic + contact Carbendazim +Mancozeb (Companion) 200 0.00 100( 90) 0.00 0.00 500 0.00 100( 90) 0.00 0.00 1000 0.00 100( 90) 0.00 0.00 Metalaxyl + Mancozeb ( Matco 8-64) 200 65.33 27.13 ( 31.38) 4.47 62.34 500 59.33 33.83 ( 35.56 ) 3.20 73.04 1000 50.33 43.86 ( 41.48 ) 2.53 78.68 Iprodine + Carbendazim (Quintal) 200 0.00 100( 90) 0.00 0.00 500 0.00 100( 90) 0.00 0.00 1000 0.00 100( 90) 0.00 0.00 PDA as control 90.00 - 11.87 - CD at 5 % 2.03 - 0.36 - CV 3.79 - 6.40 - * Average of 3 replications ** Figure in parenthesis are Sin angular transformation value
160 chlorpyriphos and thiram were highly inhibitory to T. iM+rk gSA vr% budks VªkbZdksMjek ohfjMh ds lkFk chtksipkj ;k e``nk harzianum at ( 500, 1000 and 2000 ppm) however, captan and metalaxyl at 500 ppm were comparatively mipkj ds fy, vuq’kaflr ugha fd;k tkuk pkfg,A safe to T. harzianum. References Sporulation of T. viride was completely inhibited by carbendazim, hexaconazole, carbendazim + mancozeb and iprodione + carbendazim at all the three Desai SA, Kulkarni S (2004) Effect of fungicides insecticides concentrations (Table- 2 ).Through, sporulation was and weedicides on the growth and sporulation of affected by all the fungicides, but in copper oxychloride native Trichoderma harzianum Rifai a biocontrol the inhibition of sporulation was minimum i.e 19.7, 37.0 agent. Karnataka J of Agric Sci 17: 57-62 and 53.9 at 200, 500 and 1000 µg/ml, respectively. In Desai SA, Thammiah A, Kunkalikar S. (2002) Fungicide remaining fungicides the percent inhibition of spore tolerance by Trichoderma harzianum Rifai a biocontrol agent. Karnataka J of Agric Sci 15 : 397- production by was influenced by fungicides and T. viride 398 ranged from 32.0 to 80.8 .Pandey and Upadhyay (1998) Karpagavalli S (1997). Effect of different fungicides on the reported that and were highly T. viride T. harzianum growth of Trichoderma. Indi J Plant Prot 25 : 82-83. sensitive to carbendazim and completely inhibited the Madhavi M, Kumar CPC, D Reddy Rajaram and Singh TVK sporulation at 10 ppm. Gupta etal (1999) reported that (2008) Compatibility of mutant of Trichoderma spp. the T. pseudokoningii was compatible with Dithane M- with agrochemicals. J of biol Cont 22: 51-55 45 at 0.1 percent. Desai et al. (2002) also reported that Nene YL, Thapliyal PN (1993) Fungicides in Plant Disease carbendazim + mancozeb, carbendazim, carboxin, Control Oxford and IBH publishing Company Private metalaxyl + mancozeb, mancozeb and tricyclozole at Limited New Delhi 691 p 500 and 1000 ppm inhibits the growth and sporulation Papavizas GC (1985) Trichoderma and Gliocladium : Biology of T. viride. The percent inhibition of radial growth and , ecology and potential for biocontrol. Ann Rev sporulation at 500 ppm were 2.8 to 100 percent and Phytopathol 23 : 23-54 42.3 to 100 percent similarly at 1000 ppm were 4.49 to Pan S, Bhagat P (2008) Variability in production of 100 percent and 3 to 100 percent, respectively. The 100 extracellular hydrolytic enzymes by Trichoderma spp. per cent inhibition of radial growth and sporulation was and induction of resistance in gram( Cicer arietinum). recorded upon treatment with carbendazim, companion J Biol Cont 22 : 57-66 and tricyclazole. Tiwari and Srivastava (2003) reported Pandey KK, Upadhyay JP (1998) Effect of different nutrient complete inhibition of sporulation of T. viride and T media and pH on growth and sporulation of different harzianum by thiram and carbendazim isolates of Trichoderma spp. and Gliocladium virens. Veg Sci 24 : 140- 143 Gupta SB, Thakur KS, Thakur MP, Tedia V, Singh AK, Keshry ukS QQwanuk’kdks tSls dkcsZUMkfte] gSDlkdksuktksy] Fkk;ksQusV feFkkby] AK (1999) Evaluation of different growth and media for biomass production of isolates of Trichoderma. dkWij vkDlhDyksjkbM] ekudkstsc] izksihusu] dkcsZUMkfte $ ekudkstsc] Indian Phytopath 58 : 147-149 esVkysfDly $ ekudkstsc vkSj vkbizksfM;ku $ dkcsZUMkfte dh Tiwari, B K, Srivastava KJ, (2003) Fungicidal compatibility vuq’kaflr ek=k ls de ek=k dk ijh{k.k] VªkbdksMjek ohfjMh ds with Trichoderma viride and Trichoderma harzianum . News Letter Nati Horti Res and Devel Found 23 : lfg".kqrk ds fy, fd;k x;k A dkWij vkWDlhDyksjkbM vkSj izksihuso 4-7 blds fodkl vkSj chtk.kq mRiknu dks lcls de izHkkfor fd;kA v/ Vincent JH (1947) Distortion of fungal hyphae in the presence of certain inhibitors. Nature 15: 850 ;u }kjk ;g fu"d"kZ fudyk fd bl QQwanuk’kdks dks VªkbdksMjek (Manuscript Received: 20.05.2011; Accepted: 30.06.2011) ohfjMh ds lkFk feykdj chtksipkj fd;k tk ldrk gSA dkcsZUMkfte ls vdsys ;k feykoV esa VªkbZdksMjek ij cgqr T;knk gkfudkjd izHkko
161 JNKVV Res J 45(2): 162-167 (2011)
Comparative efficacy of new and recommended insecticides against soybean defoliators and stem borers
Nanda Khandwe, Ansar Nadaf and Sandeep Sharma Department of Entomology Rajmata Vijaya Raje Scindhia Krishi Vishwa Vidyalaya R.A.K, College of Agriculture, Sehore
Abstract tons /ha respectively. It has been experienced that in last 2-3 years the soybean crop is facing various Studies were carried out during the year kharif 2007 and 2008 challenges and losing its attraction among the farmers at research field of All India Co ordinated Research Project because of reduced yield. Among the various factors on Soybean Main branch Sehore (M.P.) to evaluate the bio responsible for the low yield, the insect-pests have been efficacy of new and recommended insecticides viz., Rynaxypyr considered to be of prime importance Singh and Singh 20 SC, Spinosad 40 SC, Methomil 40 SP, Trizophos 40 EC, (1999). Singh and Verma (1992) reported 150 insect- Quinalphos 25 EC against soybean defoliators and stem pests damaging soybean in Madhya Pradesh and about borers. In the study of efficacy of new and commonly a dozen of them have been reported causing serious recommended insecticides against defoliators and stem damage to soybean from sowing to harvesting. Amongst borers of soybean concluded that, higher doses of new insecticides like Spinosad 45EC@ 187.5ml/ha, rynaxypyr 20 these, grey semilooper (Gesonia gemma Swinhoe), and SC @ 150 ml/ha were very effective and at par with commonly green semilooper (Chrysodeixis acuta Walker) defoliate recommended insecticides viz. triazophos 40 EC @ 800ml/ the plants at vegetative and grown up stages. The stem ha and quinalphos 25 EC @ 1.5ml/ha and Methomil 40SP @ fly (Melanagromyza sojae Zehnter) and girdle beetle 1kg/ha. These insecticides were found effective and recorded (Obereopsis brevis Swed.) bore the main stem and less than 0.00 to 1 larvae /mrl. and kept crop free from larval branches. population up to 60 days. Further, Spinosad45SC @ 187.5ml/ ha, recorded lowest stem tunnelling (3.10%) caused by For successful management of these insect- maggot of stem fly. Triazophos 40 EC @ 0.800 ml/ha recorded pests, it is necessary that the recommended insecticides significantly minimum plant infestation caused by girdle beetle should be economical ,effective and eco-friendly. The (15.59%). All the insecticides gave better yield than control. insecticides like quinalphos 25 EC, triazophos 40 EC The highest mean yield 1972 kg / ha was recorded with and monocrotophos 36 SC have been recommended to quinalphos 25 EC @ 1.5 lit/ha which was 636 kg (47.60 %) check the infestation of various insect pests (Singh and more than the control. other treatments gave additional yield ranged from 485 to 609 kg/ha. Spraying of quinalphos was Singh 1988, 1990). But of late, farmers and extension economical and most remunerative recorded 1: 6.2 ICBR. officers of State Government of Madhya Pradesh reported failure of some of these insecticides against some existing and new insect-pests. With these points Keywords: Chrydeixis acuta, Geosonia gemma, in mind, some new insecticides and commonly Melanagromyza sojae, Oberiopsis brevis recommended insecticides were evaluated against the major pests of soybean for their better management with Soybean [Glycine max (L.) Merrill] is now a cash crop objective to test the comparative bio-efficacy of new and and has occupied important place in agriculture and oil commonly recommended insecticides for defoliators and economy of the country. Soybean has occupied first rank stem borers of soybean. among oil seed in India since 2005 onwards. The area under soybean cultivation has increased from 8.12 million Materials and methods ha. to 8.87 million ha. and production from 7.96 million tons to 9.46 million tones i.e. about 9.2% and 18.7% in 2006-07 and 2007-08 respectively which is a speculiar The field experiment, in randomized block design with increase. In the year 2009 total area and production of nine treatments viz., Rynaxypyr 20 SC (@100ml and150 soybean is 9.05 million ha. and production 9.60 million ml/ha ), Spinosad 40 SC (@125 ml, 162.5 ml and 187.5
162 ml/ha), Methomil 40 SP (@1 kg/ha), Trizophos 40 EC at that time untreated control had highest population of (@800 ml/ha), Quinalphos 25 EC (@1500 ml) and 3.16 larvae/mrl. Earlier, Khandwe et al.(1992) reported untreated as control, was conducted during kharif season that the quinalphos (0.025%) and formothion (0.025%) of 2007 and 2008 at Research Farm of R.A.K. College are highly toxic,and both causing 100 per cent egg of Agriculture, Sehore, Madhya Pradesh. The treatments mortality of grey Semilooper, Rivula sp.Singh and were replicated four times. Soybean variety JS 335 was Singh(1994a) also reported, monocrotophos 36 SC sown on July 3rd 2007 and June 26th 2008 in the @0.036% triazophos (0.04%)and fenvalrate (0.01%) respective years in plots of 5.0 x 2.8, each having 7 rows highly toxic against the grey semilooper on soybean. 40 cm apart. Spraying of insecticides was done twice at Further, Singh (1995) reported triazophos (0.04%), 35 and 60 days after germination. Observations on acephate (0.07%), chlorpyrifos (0.05%), methomyl semiloopers were taken before treatment and five days (0.04%), ethion (0.1%) endosulfan (0.07%) and after treatment by shaking the plants on muslin cloth at quinalphos 0.05% as highly toxic against the grey three places of one meter row length of each treatment semilooper on soybean up to 15 days after treatments. and counting the number of larvae. Ten plants were In present finding triazophos (0.04% ) was also effective, marked and numbers of girdled plants by girdle beetles recorded less than 1 larvae /mrl.It is also supported by were recorded during observations. The plants were the finding of Singh and Singh (1994a) and Singh (1995). uprooted and split open vertically to record the incidence Further the population of grey semilooper of stem fly, M. sojae and the tunnel length caused by its maggots. Similarly yield from each plot was also recorded (Gesonia gemma) and green semilooper (Chrysodeixis and converted to kg/ha. acuta) was appeared between 50 to 55 days after germination in 2007 and 2008, respectively. Hence the second treatment was given at 60 days crop stage.Their Results and Discussion population ranged from 4.83 to 6.24 larvae/mrl during 2007 and 6.33 to 7.02 larvae/ mrl in 2008 at one day before the 2nd spray treatment, and their population was Effect on larval population of grey Semilooper, Gesonia identical in all the plots in both of the years. Again all the gemma (Swinhoe)) and green green semilooper, insecticidal treatments were significantly effective and (Chrysodeixis acuta (Walker) recorded less than one larvae/mrl as compared to untreated control at 5DAT. During the year 2007 The larval population of grey semilooper (Gesonia methomil 40 SP @ 1 kg/ha, trizophos 40 EC @ 800 ml/ gemma) ranged from 4.41 to 5.99 larvae/mrl one day ha recorded significantly lowest population (0.16l/mrl) before the first spray treatment in the year 2007 while it where as control plot recorded highest population (5.98 was 16.11 to 19.85 larvae/ mrl in the year 2008. There larvae/mrl) while during 2008 year methomil 40SP @ was no incidence of green semilooper (Chrysodeixis 1kg/ha, and spinosad 45EC@ 187.5ml/ha kept crop free acuta) at the time of first spray treatment when the crop from larval population. Similarly, lower doses of spinosad was only 35 days old during both the years. At 5 days 45 SC, rynaxypyr 20SC, triazophos 40 EC @ 800ml/ha after first spray treatment, all the insecticides in both the and quinalphos 25 EC @ 1.5ml/ha were also found years were significantly superior in comparison to effective and recorded less than 1 larvae /mrl. Whereas untreated control in reducing the larval population of grey control plot recorded highest population (6.66 larvae/ semilooper. During the year 2007,higer doses of new mrl).Singh and Singh (1990) reported cypermethrin insecticides ranaxypyr 20 SC @ 150 ml/ha, spinosad (0.015%), deltamethrin (0.001%) and quinalphos 40 SC @ 162.5 and 187.5 ml/ha, methomil 40 SP @ 1 (0.05%) highly toxic against the larvae of Chrysodeixis kg/ha, and already recommended triazophos 40 EC @ acuta and found free from larval population up to 30 days 800 ml/ha and quinalphos 25 EC @ 1.5 ltr/ha were very after treatment, where as monocrotophos @ 0.4kg a.i./ effective and recorded 0.00 larvae/mrl as compared to ha (Mishra et al. 1995) and cypermethrin 0.01%, untreated control (8.49 larvae/mrl), while lowest doses quinalphos 0.05%, deltamethrin 0.00125% Rajput et al. of new insecticides , spinosad 40 SC @ 125 ml/ha and (1996) have been reported effective against larvae of ranaxypyr 20 SC @ 100 ml/ha were also effective green semilooper in different parts of the country. Dubey recorded 0.99 and 1.24 larvae/mrl respectively at 5 DAT et al. (1998) reported triazophos better than microbial during the year 2007. Further, in the year 2008, again agents against the grey and green semilooper. Earlier, similar trend was observed .during first spray treatment. Singh and Singh (1990) reported cypermethrin (0.015%), Among the insecticides, Methomil 40SP @1kg/ha deltamethrin (0.001%) and quinalphos (0.05%) highly recorded significantly lowest larval population and at par toxic against the larvae of Chrysodeixis acuta and found with all the treatments recorded less than 1 larvae /mrl free from larval population upto 30 days after treatment,
163 Table 1. Effect of different insecticides against green and grey semiloopers of soybea
S.No Treatments Dose/ha First Spray at 35 DAG Second Spray at 60 DAG (Population of G.gemma ) (Population of G. gemma & C. acuta) 2007 2008 Mean 2007 2008 Mean 5 DAT 5 DAT 5 DAT 5 DAT 1. Rynaxypyr 20 SC 100ml 1.24 0.83 1.03 1.24 0.41 0.82 (1.20) (1.13) (1.16) (1.04) (0.92) (0.98) 2. Rynaxypyr 20 SC 150 ml 0.00 0.33 0.16 0.41 016 0.28 (0.70) (1.03) (0.86) (0.93) (0.85) (0.89) 3. Spinosad 40 SC 125 ml 0.99 0.89 0.94 0.49 0.25 0.74 (1.14) (1.11) (1.12) (0.99) (0.83) (0.91) 4. Spinosad 40 SC 162.5 ml 0.33 0.41 0.33 0.33 0.08 0.20 (0.89) (0.93) (0.89) (1.03) (0.75) (0.89) 5. Spinosad 40 SC 187.5 ml 0.00 0.33 0.16 0.24 0.00 0.12 (0.70) (0.92) (0.86) (0.84) (0.70) (0.77) 6. Methomil 40 SP 1 kg 0.00 0.24 0.12 0.16 0.00 0.08 (0.70) (0.84) (0.77) (0.80) (0.70) (0.75) 7. Triazophos 40 EC 800 ml 0.00 0.41 0.20 0.16 016 0.24 (0.70) (0.93) (0.81) (0.85) (0.85) (0.84) 8. Quinalphos 25 EC 1500 ml 0.00 0.49 0.29 0.41 0.24 0.32 (0.70) (0.97) (0.89) (0.92) (0.84) (0.88) 9. Control - 8.49 3.16 5.82 5.98 6.66 6.32 (2.98) (1.90) (2.44) (2.53) (2.63) (2.58) SEm± 0.11 0.12 0.08 0.03 0.05 CD at 5% 0.36 0.37 0.25 0.10 0.17 where as monocrotophos @ 0.4kg a.i./ha (Mishra et al. different parts of the country.The effectiveness of 1995) and cypermethrin 0.01%, quinalphos 0.05%, chlorpyrifos 20 EC with tank mixed cypermethrin 5 EC deltamethrin 0.00125% Rajput et al. (1996) have been has been reported against semilooper on soybean reported effective against larvae of green semilooper in
Table 2. Effect of different insecticides on the infestation of Stem Fly and Girdle beetle of Soybean S.No Treatment Doseml/ha Stem fly Girdle beetle (% stem tunneling at harvest) (% plant infestation) 2007 2008 Mean 2007 2008 Mean 1. Rynaxypyr 20 SC 100 4.41 7.08 5.74 3.74 21.36 12.55 (11.94) (15.36) (13.65) (10.77) (27.45) (19.11) 2. Rynaxypyr 20 SC 150 2.60 3.20 2.90 2.42 20.44 11.43 (9.25) (10.23) (9.74) (7.66) (26.85) (17.25) 3. Spinosad 40 SC 125 3.98 4.11 4.02 2.90 21.21 12.05 (10.81) (11.64) (11.22) (8.15) (27.37) (17.76) 4. Spinosad 40 SC 162.5 3.32 3.98 3.65 2.96 17.74 10.35 (10.44) (10.85) (10.64) (8.46) (24.85) (16.65) 5. Spinosad 40 SC 187.5 2.29 3.10 2.69 2.48 15.74 9.11 (8.50) (10.02) (9.26) (8.68) (23.30) (15.99) 6. Methomil 40 SP 1 kg 3.73 4.72 4.22 2.18 16.70 9.44 (10.08) (12.43) (11.25) (7.16) (24.04) (15.60) 7. Trizophos 40 EC 800 3.16 4.00 3.58 2.07 15.59 8.83 (10.08) (10.38) (10.19) (7.05) (23.13) (15.09) 8. Quinalphos 25 EC 1500 3.70 5.32 4.51 3.31 20.19 11.75 (10.86) (13.91) (12.38) (10.06) (26.64) (18.36) 9. Control - 9.35 15.18 12.26 10.41 44.77 27.59 (17.82) (22.84) (20.33) (18.51) (41.95) (30.23) SEm± 0.62 0.39 0.50 2.05 1.05 1.55 CD at 5% 1.95 1.16 1.55 6.19 3.08 4.63
164 Table 3. Yield and economics of new and recommended insecticides
S.No. Treatments Mean yield Additional Per cent Additional Cost of Net returns B:C ratio of both yield over increase returns insecticides (Rs/ha) years control yield over (Rs/ha) (Rs/ha) control (%) 1 Rynaxypyr 20 SC@100ml/ha 1856 520 38.9 7800 3000 4800 1 : 1.60 2 Rynaxypyr 20 SC@150 ml /ha 1906 570 42.66 8550 4200 4350 1 : 1.03 3 Spinosad 40 SC@125ml/ha 1805 469 35.10 7035 4156 2879 1 : 0.69 4 Spinosad 40 [email protected] ml/ha 1835 499 37.35 7485 5164 2321 1 : 0.44 5 Spinosad 40 [email protected] ml/ha 1945 609 45.58 9135 5884 3251 1 : 0.55 6 Methomil 40 SP@1 Kg/ha 1911 575 43.03 8625 3276 5349 1 : 0.63 7 Trizophos 40 EC@800ml /ha 1821 485 36.30 7275 1196 6079 1 : 5..0 8 Quinalphos 25 EC@1500ml /ha 1972 636 47.60 9450 1306 8144 1: 6.20 9 Control 1336 - - - - -
Rate of insecticides: Rynaxypyr 20 SC@ Rs.1840 /150 ml, Spinosad 40 SC @ Rs 1080/75ml, Methomil 40 SP@ Rs 340/250 g, Trizophos 40 EC@ Rs 400/liter, Quinalphos 25 EC@ Rs 250/liter, Labour cost @ Rs 139/day, Prices of soybean Rs 15/kg
(Yadav et al. 2001). reported tunnelled stem length was significantly less with soil application of phorate 10G @10kg/ha .followed by in carbofuran seed treatment with thiamethoxam 70WS Effect on stem fly (Zehntner) and , Melanagromyza sojae (6g/kg) and spraying of thiamethoxam 25 WG (100g/ girdle beetle, (Swedenbord) Obereopsis brevis ha). Similarly, during 2008 plant infestation caused by infestation grub of girdle beetle ranged from 2.07 to 3.74 in different treatments as compared to 10.41 % in control. In the During both years no seedling mortality was observed year 2008 the incidence of girdle beetle was increased at 7 to10 DAG. Stem tunnelling caused by maggots of recording 15.59 to 21.36 % in different treatments as stem fly ranged from 2.29 to 4.72 % during 2007 while compared to 44.77% in control. Methomyil 40 SP and 3.10 to 5.32% during 2008 in different insecticidal trizophos 40 EC recorded significantly minimum plant treatments as compared to 6.12 and 15.18% respectively infestation 2.07 and 2.08 respectively and both were at stem tunnelling in untreated control. In the year 2007 par with all insecticidal treatments, except untreated spinosad 45 SC @125, 162.5 and 187.5 ml/ha, control in the year 2008, while Triazophos 40 EC methomyil 40 SP @ 1 kg/ha, trizophos 40 EC @ 800 ml/ recorded significantly minimum plant infestation ha and quinalphos 25 EC @ 1.5 ltr/ha were effective in (15.59%), which was at par with all the remaining reducing stem tunneling, while in 2008, spinosad 45 SC insecticidal treatments in the year 2008. Earlier Parsai @ 187.5ml/ha, recorded lowest stem tunnelling (3.10%) et al.(1990) reported that the crop damage by girdle which was at par with all the treatment except lowest beetle was lowest with quinalphos (2.9%) followed by dose of rynaxypyr 20SC@ 100 ml/ha which recorded phosalone (3.19%) and endosulfan(4.53%). (Rajput et 7.08 per cent tunnelling. Stem tunnelling was very low in al. (1996) and Salunke (1999) reported that a specified general during the year 2007 and 2008 as against 43.70 no. of plant were damaged by girdle beetle when phorate to 50.28 per cent reported by the various workers (Singh 10G was applied at 10 & 15 kg/ha in the furrows at the and Singh, 1990; Kundu and Shrivastava 1991). Earlier time of sowing. Similarly Dahiphale et al. (2007) reported monocrotophos and quinalphos (Singh and Singh 1990), that sowing application of phorate showed significantly quinalphos (Parsai et al. 1991) and monocrotophos less damaged followed by carbofuran soil applicaion (Kundu and Shrivastava 1991; Latha et al.1993) have (30kg/ha), imidacloprid spray (100ml/ha) and chlorpyrifos been reported effective in checking the plant infestation (1.5ltr/ha). and stem tunnelling caused by maggots of stem fly. Seed treatment with EC formulation of endosulfan quinalphos Yield and Economics and chlorpyrifos @ 15ml/kg seed had been reported effective against the infestation of Melanagromyza sojae in Orissa (Sontakke 1995). Further, Sharma et al. (1997) Grain yield was statistically identical in all treatments, found monocrotophos and fenvalerate as effective in including untreated control. Yield ranged from 1567.67 reducing larval population of agromyzids.Salunke et al. to 2025.00 kg/ha in 2007 and 1106 to 1988.76 kg/ ha in (2004) reported that per cent stem tunneling was lowest 2008. During 2007 grain yield was maximum in in phorat 10 G @10kg/ha.and Dahiphale et.al (2007) quinalphos 25 EC @1.5 lit /ha and in 2008 it was
165 maximum in spinosad 45SC @ 187.5ml/ha and minimum 60 fnuksa rd Qly bfYy;ksa ds izdksi ls lqjf{kr jghA u;s dhVuk’kdksa in untreated control during both years. Other remaining 20 187-5 insecticidal treatments also recorded good yield ranged esa LihukslsM ,l-lh fe-yh-@gsDVj ls mipkfjr iz[kaMksa from 1800 to 1975 kg/ha in 2007 and 1720 to1847.50 esa ruk eD[kh ls izdksfir ruk lqjax lcls U;wure ¼3.10%½ jghA kg/ha in 2008 respectively. On the basis of two years blh izdkj pØHk``ax dhV dk izdksi Vªk;tksQkWl 40 ,l-lh- 0.800 data (Table 3) it is concluded that all the insecticides gave fe-yh-@gsDVj }kjk mipkfjr iz[kaM esa lcls U;wure ¼15.59%½ better yield than control.The highest mean yield 1972 kg / ha was recorded with quinalphos 25 EC @ 1.5 lit/ha ik;k x;kA lokZf/kd mit ¼1972 fdyks@gsDVj½ fDoukyQkWl 25 bZ- which was 636 kg (47.60 %) more than the control. Other lh- }kjk mipkfjr iz[kM esa izkIr gqbZ tks vuqipkkfjr iz[kaMksa ls treatments gave additional yield ranged from 485 to 609 47.60% ¼636 fdyks@gsDVj½ vf/kd jghA vU; dhVuk’kdksa esa kg/ha. Spraying of quinalphos was economical and most remunerative recorded 1: 6.2 ICBR. This was followed mit 485-609 fdysk@gsDVj] vuqipkfjr iz[kaM ls vf/kd vkadh by triazophos 40 EC (1: 5.0) and lowest dose of ranaxypyr xbZA vkfFkZd n``````f"V ls lokZf/kd ykHk ¼8144 :i;ss@gsDVj½ rFkk 20 SC (1: 1.6). ykHkykxr vuqikr 1:6-2 fDoukyQkWl 25 bZ-lh- esa izkIr gqvkA Higher grain yield of soybean as a result of effective chemical control of insect pests with quinolphos References (0.05%) and monocrotophos (0.04%) (Singh and Singh, 1990),endosulfan (Venkateshan and Kundu, 1994), Dahiphale KD, Suryawans DS, Kamble SK, Pole SP, Madrap monocrotophos and quinolphos (0.05%) (Rajput et. al., IA (2007) Effect of new insecticides against the 1996) quinolphos (0.05%) (Patil et a .2003) have been l control of major insect pests and yield of soybean reported in different areas of soybean in India. Further [Glycine max (L.)Merrill]. Soybean Res 5: 87-90 reducing of 30.26% grain yield by Melanagromyza sojae Duby MP, Singh KJ, Singh OP, Chaturvedi S (1998) Bio- (Singh and Singh, 1990) has been reported at Sehore. efficacy and economics of microbial agents in the field against major insect pests of soybean in Madhya Yadav et al. (2001) reported maximum grain yield Pradesh. Crop Res 15(2): 256-259 in plot treated with chlorpyrifos 20 EC with tank mixed cypermethrin 5 EC followed by quinalphos 20AF and Kundu G, Shrivastava KP (1991) Management of soybean stem fly, (Zehnt.) in plains of lambda cyhalothrin 5 EC soybean. In present findings Melanagromyza sojae North India. J. Insect Sci 4(1): 50-53 among different insecticidal treatments, Quinalphos 25 Latha CRP, Sriramul M, Babu TR (1993) Evaluation of EC@1500ml /ha recorded highest grain yield (mean of insecticides against insect pest complex of soybean, both years1972kg/ha),This is also supported by the Glycine max (L.) Merrill. Pesticide Res J 5(2): 212- findings of Yadav et al.(2001) Dahiphale et.al (2007) 224 reported that the seed yield of soybean significantly Mishra BK, Mandal SMA, Mishra PR (1995) Studies on increased with the application of phorate 10G @ 10 kg/ chemical control schedule for soybean green ha followed by thiamethaxam spray @ 100g/ha. semilooper, Chrysodeixis acuta (Walker) and leaf miner, Biloba subsecivella Zeller. J Insect Sci 8(1): 73-75 u;s ,oa iwoZ vuq’kaflr dhVuk’kd tSls jsukDlhik;j 20 ,l-lh- Parsai SK, Choudhary R, Deshpande R (1991) Efficacy and LihukslsM 40 ,l-lh-] feFkksfey 40 ,l-ih-] Vªk;tksQkWl 40 bZ-lh- economics of soybean stem fly, Melanagromyza sojae (Zehntner). Bhartiya Krishi Anusandhan ,oa fDoukyQkWl 25 bZ-lh- dk lks;kchu ds iŸkh Hk{kd ,oa ruk Patrika 6(2): 85-90 Nsnd dhVksa ds fo:) izHkko Kkr djus ds fy, iz;ksx vf[ky Hkkjrh; Parsai SK, Shrivastava SK, Shaw SS (1990) Comparative vuqla/kku ifj;kstuk ds vUrxZr] vkj-,-ds-—f"k egkfo|ky;] lhgksj efficacy and economics of synthetic pyrethroids in the management of soybean girdle beetle. Current ds —f"k iz{ks= ij [kjhQ 2007 ,oa 2008 esa fd;sA ijh{k.k ds Res 19(8): 134-136 }kjk Kkr gqvk fd u;s dhVuk’kd] jsukDlhik;j 20 ,l-ih- 150 Rajput RR, Singh OP, Das SB, Saxena Ashok, Saxena A 40 187-5 (1996) Bio-efficacy of granular insecticides against fe-yh-@gsDVj ,oa LihukslsM bZ-lh- fe-yh-@gsDVj lokZf/ stem fly and defoliators in soybean. Bhartiya Krishi kd :i ls izHkko’kkyh jgs ,oa iwoZ vuq’kaflr dhVuk’kd tSls Annusandhan Patrika 11: 91-97 Vªk;tksQkWl 40 bZ-lh- 800 fe-yh-] fDoukyQkWl 25 bZ-lh-] 1.5 Salunke SG (1999) Field screening and efficacy of some granular insecticides against seedling insect pest of 40 1 fe-yh-] ,oa feFkksfey ,l-ih- fdyks@gsDVj ls lakf[;dh :i soybean [Glycine max (L.)] M.Sc. Thesis Marathwada ls lerqY; jgsA ;s lHkh dhVuk’kd izHkkoh jgs ,oa buesa gjh ,oa Hkwjh Agricultural University Parbhani v/kZdq.Myd bfYy;ksa dh la[;k 'kwU; ls ,d bYyh@ehVj ikbZ xbZ ,oa Salunke SG, Munde AT, More DG, Mane PD, Bidgire US
166 (2004) Efficacy of some granular insecticides against Melanagromyza sojae (Zehntner) and Chrysodeixis insect pests of soybean seedlings. J Soils and crop acuta (Walker) on soybean. J Insect Sci 3(1): 77-82 14(1): 156-162 Sontakke BK, Mukherjee SK, Mishra PR (1995) Efficacy of Sharma D, Gargav VP, Sharma U (1997) Effect of insecticides insecticidal seed dressing against soybean stem fly, on the incidence of soybean stem fly [(Ophiomyia Melanagromyza sojae (Zehntner). Indian J Plant Prot phaseoli (Tryon)] Crop Res13(2): 437-443 23: 35-37 Singh OP, Singh KJ, Nema KK (2000) Efficacy of some seed Venkatesan T, Kundu GG (1994) Yield infestation relationship dressing and granular insecticides against major and determination of economic injury level of stem insect pests of soybean. Pestology 24(1): 8-11 fly, Melanagromyza sojae (Zehnter) infesting Singh, OP, Singh KJ (1990) Efficacy and economics of some soybean. J Ent Res 18(3): 265-270 emulsifiable concentrate insecticides against (ManuscriptYadav MK, Received:Matkar SM, 30.05.2011; Sharma AN, Accepted: Billlore 18.08.2011) M, Kapoor Patidar KN (2001) Efficacy and economics of some new insecticides against defoliators and stem borers of soybean [Glycine max (L.) Merrill]. Crop Res Hisar 21(1): 88 – 92
167 JNKVV Res J 45(2): 168-171 (2011)
Correlation of yield and attributing characters in Dolichos bean under conditions of Jabalpur, Madhya Pradesh
D.P. Sharma, Niranjan Dehariya, Rajani Bisen and B.P. Bisen Krishi Vigyan Kendra Jawaharlal Nehru Krishi Vishwa Vidyalaya, Jabalpur 482004 (MP)
Abstract Materials and methods
The present investigation was carried out with 25 treatments The investigations were conducted at Vegetable viz., 23 genotypes and 2 checks in Randomized Complete Research Farm, Department of Horticulture, JNKVV, Block Design (RCBD) using three replications of Dolichos bean Jabalpur (M.P.). A total of 25 treatments (23 genotypes at JNKVV, Jabalpur. In all sixteen characters were considered + 2 checks), were arranged and allotted in plots randomly for correlation analyses. The results indicated that pod yield using RCBD with 3 replications. Soil of the experimental per plant recorded highly significant and positive correlation field was sandy loam with normal organic matter and with number of spikes per plant, days taken to first flowering, good drainage. Soil has low organic carbon (0.55), days taken to 50 % flowering, days taken to first pod harvest, available nitrogen, available phosphorus and high 25 pod weight, pod length, pod width, pod girth and 1000 potassium with soil pH 7.3. The observations were seed weight and number of flowers per cluster showed recorded from five randomly selected plants in each significant negative association. The chances of upgrading replication. Correlation coefficient was calculated in all genotype of dolichos bean can be achieved by simultaneous possible combinations taking all the characters in to selection of these characters. consideration at genotypic, phenotypic and environmental levels by using the formula as proposed Key words: Correlation, Dolichos bean, selection by Miller et al. (1958).
Dolichos bean (Dolichos lablab L.), vegetable is grown Results and discussion for fresh consumption as well as dry seeds are used as pulse. Regional preferences are predominantly playing Results indicated that in general, genotypic correlation an important role in its cultivation. The green pods are coefficient were of higher magnitude than the mostly consumed under South Indian conditions, corresponding phenotypic ones and similarly, phenotypic whereas white and green pods are preferred in eastern correlation coefficient was higher as compared to and northern parts, respectively. Perennial climbing environmental correlation coefficient (Table 1). The strain with long pods and annual bushy erect with smaller findings were in agreement to Dahiya et al. (2000), Tyagi pods are two groups of Dolichos lablab species available et al. (2000), Santosh et al. (2002) and Saini et al. (2005). in India. The results of phenotypic correlation coefficient have been discussed only as the genotypic and environmental A wide range of variability exists in pod shape, correlations were mostly influenced by the environmental size and colour and other agronomic characters in conditions, hence phenotypic correlation will give the Dolichos bean. The most important among attributes of correct idea about the association between two variables. a plant is its yielding ability. For rational approach to the improvement of yield, it is essential to have detailed Highly significant and positive correlation of pod information on the association among different yield yield per plant was recorded with number of spikes per plant, days taken to first flowering, days taken to 50 % components. Correlation of quantitative attributes would flowering, days taken to first pod harvest, 25 pod weight, help in choosing component characters that are positively pod length, pod width, pod girth and 1000 seed weight. correlated, hence the investigation was conducted.
168 (kg) Pod yield/ha 0.772 0.726** 0.672 0.642** 0.664 0.658** 0.677 0.670** 0.688 0.660** 0.596 0.584** 0.649 0.627** 0.235 0.208* 0.608 0.607** 1.000 0.999** 1.000 0.999** 1.000 1.000 -0.875 -0.831** -0.291 -0.199 -0.036 -0.019 -0.090 -0.087 Pod yield/ 0.770 0.723** 0.669 0.639** 0.660 0.654** 0.675 0.667** 0.685 0.657** 0.593 0.580** 0.644 0.622** 0.235 0.208* 0.606 0.605** 1.000 0.999** 1.000 1.000 plot(kg) -0.875 -0.830** -0.289 -0.198 -0.043 -0.019 -0.089 -0.087 Pod yield/ 0.77 0.723** 0.669 0.639** 0.66 0.653** 0.675 0.667** 0.685 0.657** 0.593 0.580** 0.644 0.622** 0.234 0.208* 0.606 0.605** 1.000 1.000 -0.874 -0.830** -0.289 -0.197 -0.044 -0.019 -0.089 -0.087 plant (g) plant 0.098 0.095 0.159 0.138 0.187 0.182 0.346 0.329** 0.174 0.095 0.537 0.250** 0.026 0.024 1.000 1.000 -0.069 -0.063 -0.258 -0.239* -0.076 -0.074 -0.292 -0.281** -0.141 -0.126 in pod in Protein% wt. 0.465 0.434** 0.645 0.617** 0.646 0.640** 0.700 0.691** 0.407 0.224* 0.548 0.526** 0.672 0.656** 0.426 0.412** 0.491 0.437** 1.000 1.000 seed 1000 -0.638 -0.607** -0.332 -0.228* (mm) 0.151 0.11 0.341 0.320** 0.322 0.269** 0.261 0.230* 0.271 0.052 0.442 0.355** 0.252 0.227* 0.392 0.375* 1.000 1.000 -0.301 -0.254** -0.385 -0.198 Pod girth Pod 0.473 0.450** 0.459 0.452** 0.513 0.487** 0.339 0.332** 0.823 0.575** 0.437 0.426** 1.000 1.000 (cm) -0.428 -0.409** -0.435 -0.265** -0.172 -0.088 Pod width Pod Pod 0.552 0.530** 0.468 0.444** 0.655 0.632** 0.523 0.507** 0.388 0.209* 0.772 0.743** 1.000 1.000 (mm) -0.623 -0.577** -0.124 -0.086 length (g) 0.525 0.479** 0.363 0.327** 0.533 0.509** 0.349 0.323** 0.186 0.123 1.000 1.000 -0.557 -0.505** -0.343 -0.241** 25 pod wt. pod 25 pod 0.238 0.119 0.106 0.037 0.327 0.164 0.343 0.181 0.288 0.053 1.000 1.000 -0.288 -0.17 Seeds/ 0.247 0.181 0.131 0.9** 1.000 1.000 -0.216 -0.132 -0.373 -0.261** -0.375 -0.238** Pods/ cluster 0.596 0.564** 0.792 0.742** 0.896 0.877** 1.000 1.000 1st pod 1st harvest -0.815 -0.768** Days to Days 50% 0.653 0.613** 0.873 0.832** 1.000 1.000 -0.800 -0.755** Days to Days flowering 1st 0.607 0.565** 1.000 1.000 -0.743 -0.686** Days to Days flowering plant 1.000 1.000 -0.774 -0.732** Spikes/ 1.000 1.000 cluster Flowers/ P P P P P P P P P P P P P P P P G G G G G G G G G G G G G G G G Phenotypic and genotypic correlation coefficient for sixteen characters in Indian bean genotypes bean Indian in characterssixteencoefficient for correlation genotypic and Phenotypic flowering pod harvest t t Table 1. Table Characters Flowers/cluster Spikes/plant 1s to Days 50%flowering to Days to1s Days Pods/cluster Seeds/pod (g) weight pod 25 (cm)length Pod (mm) width Pod girth (mm)Pod (g) seed weight 1000 % pod in Protein plant(g) yield/ Pod (kg) /plot yield Pod (kg) /ha. yield Pod 1% level **Significant at level 5% at *Significant
169 Negative and significant association was recorded with showed positive and significant correlation with 1000 number of flowers per cluster. These findings are in close seed weight, pod yield per hectare, pod yield per plot, proximation to that of Samal et al. (1995), Mishra et al. pod yield per plant, pod length, pod width, 25 Pod weight, (1996), Mehta et al. (1997), Arya and Rana (1999), protein % in pod and pod girth. Significant and negative Chand (1999), Tikka et al. (2003), Yadav et al. (2003), association was noted with pods per cluster. These Pan et al. (2004), Singh et al. (2004), Anbumalarmathi findings are in agreement with the findings of Arya and et al. (2005), Mittal and Singh (2005) and Ali et al. (2005). Rana (1999). Thus, indicating that the importance of these characters in selection. However, pods per cluster showed significant negative association with pod width, 25 Pod weight and The number of flowers per cluster showed 1000 seed weight. negative and significant association with pod yield per hectare, pod yield per plot, pod yield per plant, days taken Seed per pod showed significant and positive to 50 % flowering, number of spikes per plant, days taken correlation with protein % in pod, 1000 seed weight and to first pod harvest, days taken to first flowering, 1000 pod length. It indicates that increase in these characters seed weight, pod length, 25 Pod weight, pod width and would result in increase in seeds per pod. These findings pod girth. This indicating that increase in one trait may are in agreement with the findings of Tikka et al. (2003), generally be accompanied by corresponding decrease Yadav et al. (2003) and Singh et al. (2004). Weight of in the other. These results are in close conformity with 25 pod expressed significant and positive correlation with the findings of Reddy (1992). pod length, pod yield per hectare, pod yield per plot, pod yield per plant, pod width, 1000 seed weight and pod Significant and positive correlation of number of girth, Whereas, protein % in pod showed negative spikes per plant was recorded with pods per cluster, pod association. This is indicative of fact that the increase in yield per hectare, pod yield per plot, pod yield per plant, 25 Pod weight may result in increase in pod length, pod days taken to 50 % flowering, days taken to first pod yield per hectare, pod yield per plot, pod yield per plant, harvest, days taken to first flowering, 1000 seed weight, pod width, 1000 seed weight and pod girth. Similar pod length, 25 pod weight and pod width. Selection for findings are also reported by Samal et al. (1995), Mishra yield is increased due to increase in number of spikes et al. (1996), Tikka et al. (2003), Yadav et al. (2003), per plant. These results are also in agreement with the Singh et al. (2004) and Anbumalarmathi et al. (2005). findings of Prakash et al. (2003), Venkatesan (2003), Anbumalarmathi et al. (2005) and Ali et al. (2005). Highly significant positive correlation of 1000 seed weight was exhibited with pod yield per hectare, pod yield Days to first flowering showed significant and per plot and pod yield per plant. Similar findings are also positive association with days taken to 50 % flowering, reported by Chand (1999), Tikka et al. (2003), Singh et days taken to first pod harvest, pod yield per hectare, al. (2004), Anbumalarmathi et al. (2005) and Mittal and pod yield per plot, pod yield per plant, 1000 seed weight, Singh (2005). Pod yield per plot showed positive and pod width, pod length, 25 pod weight and pod girth. highly significant correlation with pod yield per hectare. Findings were similar to the results obtained by Samal These findings are supported by Dhiman et al. (1991) et al. (1995) and Tikka et al. (2003). Selection for days and Samal et al. (1995). to first flowering may lead to increase in characters showing positive association and ultimately increase in Thus, it can be concluded that correlation studies pod. indicated chances of upgrading dolichos bean genotype by simultaneous selection through number of spikes per Days to 50% flowering expressed significant and plant, days taken to first flowering, days taken to 50% positive correlation with days to first harvest, pod yield flowering, days taken to first pod harvest, 25 pod weight, per hectare, pod yield per plot, pod yield per plant, 1000 pod length, pod width, pod girth, and 1000 seed weight. seed weight, pod length, 25 pod weight, pod width and pod girth. Whereas, significant negative correlation was shown with pods per cluster. The results confirmed the lse ¼Mksyhdksl chu½ dh 23 iztkfr;ksa ¼thuksVkbi½ ds ewY;kadu ds findings of Samal et al. (1995), Arya and Rana (1999) i'pkr ;g ik;k x;k fd mUu;u gsrq lrr~ lysD'ku t:jh gS A and Mittal and Singh (2005). Pod yield can be increased izR;sd ikS/ks ls mit c<+kus gsrq Qyh la[;k] ipkl izfr'kr Qwyksa ds by increasing days to 50 % flowering indirectly by igq¡p le;] 25 Qfy;ksa dk otu] Qyh dh yEckbZ] Qyh dh pkSM+kbZ] increasing pods per cluster. Days to first pod harvest
170 1000 nkuksa dk otu vkfn xq.kksa dk /kukRed izHkko feyk A Qwyksa dh Pan RS, Singh AK, Rai Mathura, Krishnaprasad VSR, and Kumar S (2004) Genetic variation and characters la[;k izfr DyLVj xq.k dk _.kkRed izHkko feyk A association in photoinsenstive Dolichos bean (Lablab purpureus L. Sweet) Veg Sci 31 (1):22-25 References Prakash M, Ganesan J, Venkatesan M (2003) Genetic variability, heritability and genetic advance analyses in cowpea (Vigna unguiculata (L.) Walp) Legume Res Ali F, Sikder B, Roy AK and Joarder OL (2005) Correlation 26 (2): 155-156 and genetic variation of twenty different genotype of Reddy M, Viswanath SR, Satyan BA, Wali MC (1992) Genetic Lablab bean (Lablab purpureus (L.) Sweet) variability, heritability and genetic advance for yield Bangladesh J Bot 34 (2): 125-128 and yield contributing characters in field bean (Lablab Anbumalarmathi J, Sheeba A, Deepasankar P (2005) Genetic purpureus L. Sweet) Mysore J Agric Sci 26 (1):15- variability and interrelationship studies in cowpea 20 [Vigna unguiculata (L.)] Res Crops 6(3): 517-519 Samal KM, Senepati N, Lanka D, Nandi A, Tripathy P (1995) Arya PS, Rana Ajay (1999) Study of direct and indirect Varietal performance, genetic variability and influences of some yield traits on green pod yield in correlation in rajmah Legume Res 18 (3-4):223-227 french bean (Phaseolus vulgaris L.) Adv Horti Saini DD, Choudhary SPS, Singh NP, Singh RV, Singh Jabar Forestry 6: 99-106 (2005) Estimation of genetic parameters in Chand Poorna (1999) Character association and path analysis clusterbean [Cyamopsis tetragonoloba (L.) Taub] Arid of rajmah Madras Agri J 85(3/4):188-190 legume Sust Agri Trade 1: 106-110 Dahiya A, Sharma SK, Singh KP, Kumar A (2000) Correlation Santosh Kumar, Tyagi ID, Kumar Sunil, Singh B (2002) studies in french bean (Phaseolus vulgaris L.) Annals Analysis of fodder yield components in segregation Biol 16(2):201-204 generation of cowpea Prog Agri 2 (1):22-55 Dhiman D, Svetleve D, Lonanov J (1991) Correlation and path Singh SP, Kumar R, Joshi AK, Singh Bhagat (2004) Genetic coefficient analysis of some quantitative characters architecture of yield traits in Cowpea (Vigna in french bean General Selektsiya 24 (4): 221-225 unguiculata (L.) Walp) Adv Plant Sci 17(2): 495-502 Mehta DK, Kohli UK, Kumar A (1997) Genetic variability and Tikka SBS, Chauhan RM, Parmar LD, Solanki SD (2003) association of horticulture traits and resistance to Character interrelationship in grain type Indian bean anthracnose in french bean J Hill Res 10 (2): 87-89 Adv Arid Legume Res 136-139 Mishra HN, Killai D, Mishra RC (1996) Character association Tyagi PC, Kumar N, Agrawal MC, Kumar N (2000) Genetic and path coefficient analysis in pole type french bean variability and association of component characters Environ Ecol 14 (1): 103-106 for seed yield cowpea Central Soil and Water Miller DA, Williams JC, Robinson HF, Comstock KB (1958) Conservation Research Training Institute Dehradun Estimates of genotypic and environmental variances 248-251 and co variances in upland cotton and their Yadav KS, Yadava HS, Naik ML (2003) Correlation and path implication in selection Agron J 50 : 126-131 analyses in early generations of cowpea Ind J Pulses Mittal VP, Singh Paramjit (2005) Component analysis of seed Res 16(2): 101-103 yield and other characters in cowpea J Arid Legume 2 (2): 408-409 (Manuscript Received: 30.03.2011; Accepted: 05.06.2011)
171 JNKVV Res J 45(2): 172-174 (2011)
Effect of different temperatures and desiccants on quality of yellow button type chrysanthemum flowers
Beena Nair, Krishna Pal Singh and Vidhya Sankar M. Department of Floriculture and Landscaping K N K College of Horticulture Rajmata Vijaya Raje Scindhia Krishi Vishwa Vidyalaya Mandsaur 458001 (MP)
Abstract Material and methods
Studies were conducted to standardize the suitable desiccants The present investigation was conducted at the for hot air oven drying of yellow button type chrysanthemum laboratory of Department of Floriculture and (Dendranthema grandiflora Tzvelev) for improved display Landscaping, KNK College of Horticulture, Mandsaur quality of flowers. The experiment was laid out in factorial (MP) during 2008-2009. The experiment was laid out in CRD with five desiccants ., silica gel, calcium chloride, viz a completely randomized design with factorial concept copper sulphate, sand and borax and three drying (Factorial CRD) and the treatments were replicated temperatures viz., 40 0C, 50 0C and 60 0C. The overall quality of dried flowers was judged by a panel of 5 judges and ranked thrice. The temperature inside the laboratory ranged 0 based on their appearance, colour and texture. Among the between 25±5 C with Relative Humidity between 60 and different desiccants silica gel embedded flowers scored the 70%. Chrysanthemum flowers were harvested from the maximum points for good appearance (7.367) whereas, the floriculture green house when they attained the flowers embedded in copper sulphate scored the maximum commercial stage of harvest (Pandya and Saxena 2001). points for bright flowers (7.144). The maximum points for Flowers with uniform stem length were weighed and smooth petal texture at all the drying temperatures were placed in the aluminium trays containing the desiccants observed in silica gel embedding (7.367). The overall quality viz., silica gel (e1), calcium chloride (e2), copper sulphate of flower petals was better maintained (aesthetically (e3), sand (e4) and borax (e5). The aluminium trays acceptable flower shape, size and texture) at a low containing the desiccants and flowers were subjected temperature (40 0C) for all the desiccants tried. 0 0 to three drying temperatures viz., 40 C (t1), 50 C (t2) 0 and 60 C (t3) until the flowers attained a constant weight. Key words: Embedding materials, temperature, quality A panel of 5 judges assessed the quality parameters of dried flowers like appearance, colour and texture by means of sensory evaluation by scoring on a ten-point Flowers have been associated with mankind from the scale. The appearance of flowers was scored as 1-3 dawn of civilization and in the modern era they have (deformed), 4-6(shrunk), 7-9 (normal), the flower colour become an integral part of human life as love for flowers was scored as 1-3(dull), 4-6(normal), 7-9 (bright) and is a natural instinct. Dried flowers are eco-friendly, the flower texture as 1-3 (brittle), 4-6 (rough) and 7-9 biodegradable and retain their beauty as well as (smooth). Based on the cumulative scores ranks were everlasting value (Puri 1995). Dried flowers look near to given and best treatment combination was observed on natural and are available year round as they can resist the basis of improved physico-chemical aspects of dried the heat of summer and cold of winter and can be yellow button type chrysanthemum flowers. cherished for years. Thus drying can be a creative work for preserving the flowers in their actual shape for a much Results and Discussion longer period. Button type chrysanthemum flowers are more popular among the florists as they are available in wide spectrum of colours, shapes, sizes and forms. The quality of dried flowers was affected by the Keeping this in view, the present investigation was desiccants used, the drying duration and temperature. undertaken to standardize the most suitable desiccant The chrysanthemum flowers dried at a lower temperature 0 for hot air oven drying of Yellow button type (40 C) were qualitatively superior by maintaining a better chrysanthemum flowers.
172 flower shape, size and texture than the flowers dried at a comparatively higher temperature (50 0C and 60 0C) with all the desiccants tried (Table 2). The mean 6.800 3.667 6.133 4.867 6.267 maximum moisture loss took place at 60 0C followed by Mean(e)
0 ) 50 C. The drying process is characterized by the 3
simultaneous transfer of heat and mass (water). The C (t 0 6.533 3.033 5.733 4.167 5.467 4.987 0.258 (t x e) x (t 0.0894 water from the internal parts of the material is transferred 60 to the surface adequately. Gradually the moisture content ) 2 at the surface decreases as the drying time increases. Texture C C (t 0 As a result less free water is available at the surface (e) 6.767 3.733 6.133 5.033 6.533 5.640 0.0516 0.1492 which leads to depletion of available moisture at the 50
surface and leads to falling rate drying period. ) 1 C C (t (t) 0 7.100 4.233 6.533 5.400 6.800 6.013 0.0400 0.1156
Reduction in flower diameter 40
The silica gel embedded flowers recorded the minimum
0 0 7.100 4.344 7.144 6.878 6.411 flower size reduction i.e., 0.13, 0.17 and 0.21 at 40 , 50 Mean(e) and 60 0C respectively (Table 1) and the maximum ) moisture loss at all the drying temperatures viz. 400, 500 3 C C (t 0 6.367 3.233 6.533 6.367 5.833 5.667 0.264 (t x e) x (t 0.0915 60 Reduction in diameter (cm) yellow flowers after Table 1. ) Colour drying 2 C C (t 0 (e)
0 0 0 7.000 4.567 6.967 6.833 6.467 6.367 0.0528 0.1526
Factors/treatments 40 C 50 C 60 C Mean 50 (t ) (t ) (t ) (e)
1 2 3 ) 1 C C (t (t) Silica gel (e1) 0.13 0.17 0.21 0.17 0 7.933 5.233 7.933 7.433 6.933 7.093 0.0409 0.1182 Calcium chloride (e2) 0.36 0.36 0.39 0.37 40
Copper sulphate (e3) 0.16 0.23 0.24 0.21
Sand (e4) 0.21 0.24 0.26 0.24
Borax (e5) 0.26 0.28 0.30 0.28
Mean (t) 0.23 0.26 0.28 7.367 4.200 6.900 6.900 6.667 Mean (e) Mean Treatments SE(m) C.D. at 5% SE(d) ) Temperatures (t) 0.0060 0.0172 0.0084 3
Desiccants (e) 0.0077 0.0222 0.0109 C (t 0 6.833 2.967 6.000 6.033 5.733 5.513 0.434 (t x e) x (t
Interaction (t x e) 0.0133 N.S. 0.0189 0.1503 60 ) 0 2 and 60 C. Singh et al. (2004) reported that flower size Appearance C C (t 0 reduction was due to moisture loss from the cells. Hence, (e) 7.200 4.100 6.800 6.900 6.667 6.333 0.0868 0.2507 reduction in flower size was higher at elevated 50 temperatures. Aravinda and Jayanthi (2004) reported ) that the button type chrysanthemum flowers treated with 1 C C (t (t) silica gel and sand retain their shape even after drying. 0 8.067 5.533 7.900 7.767 7.373 7.373 0.0672 0.1942
Similarly Bhalla et al. (2006) reported that silica gel 40 embedding of chrysanthemum resulted in high quality flowers and better retention of flower size. Desh Raj and ) ) ) ) ) 1 2 3 4 5
Gupta (2003) reported silica gel as the best absorbent (e (e (e (e (e (t) for removal of moisture from the flowers and foliages.
Singh and Dhaduk (2005) reported silica gel as the fastest desiccant without any deterioration in flower quality. In the present study it was observed that silica gel embedded flowers scored the maximum points for Table 2 Sensory evaluation of Yellow flowers Yellow of evaluation Sensory 2 Table Factors/Treatments Silica gel Calcium chloride Copper sulphate Sand Borax Mean Treatments SE(m) C.D. at 5%
173 good appearance (7.367) followed by the flowers x;k Fkk vkSj mudh mifLFkfr] jax vkSj cukoV ds vk/kkj ij LFkku embedded in copper sulphate and sand which were at desiccants par (6.900). Among the interaction effect, silica gel at 40 ij jgh A fofHkUu flfydk tSy ,EcsMsm Qwy ds vykok 0C scored the maximum points (8.067) for good vPNh mifLFkfr gS (7.367) ds fy, vf/kDre vad vftZr fd;s appearance. Safeena et al. (2006) reported that tcfd] dkWij lYQsV esa ,EcsMsM Qwy mTtoy Qwy (7.144) ds fy, embedding the flowers in silica gel prevents the direct vf/kDre vad vftZr fd;s A lHkh rkieku ij fpduh iŸkh cukoV removal of moisture from flowers and acts as an intermediate and consequently prevents the shrinkage ds fy, vf/kDre vad flfydk tSy ,EcsfMax (7.367) esa euk;k x;k of the flower and degradation of colouring pigments that A Qwyksa dh ia[kqfM+;ksa dh lexz xq.koŸkk dks csgrj dh dksf'k'k dh could take place when petal tissues are exposed to high desiccants ds fy, ,d de rkieku (40 0C) esa cuk, j[kk x;k temperatures without embedding Fkk aesthetically Lohdk;Z Qwy vkdkj] vkdkj vkSj cukoV A The present investigation revealed that at lower temperature (40 0C) the quality attributes like smooth petal texture, proper shape and size of the embedded References flowers were better as compared to the flowers embedded at a comparatively higher temperatures (50 Aravinda K, Jayanthi R (2004) Standardization of drying 0 0 C and 60 C). In terms of colour the flowers embedded techniques for chrysanthemum (Dendranthema in copper sulphate scored the highest points (7.144) for grandiflora Tzvelev cv. Button type Local) flowers. J brighter looking flowers followed by the flowers Ornamental Hort 7 (3-4): 370-375 embedded in silica gel (7.100). Westland (1992) reported Bhalla R, Moona, Dhiman S R, Thakur K S (2006) that silica gel and sand maintained better quality flowers Standardization of drying techniques of and retained brighter flowers. Peggy (1978) reported chrysanthemum (Dendranthema grandiflora silica gel as the most appropriate desiccant for proper Tzvelev). J Ornamental Hort 9 (3):159-163 colour retention of flowers. The flowers embedded in Bhutani J C (1995) Drying of flowers and floral craft. Advances silica gel recorded the maximum points for smooth petal in Horticulture 12:1053-1058 texture of chrysanthemum flowers (6.800). Among the Desh Raj, Gupta RK (2003) Standardizing dehydration interaction effects, the flowers embedded in silica gel at technology for ornamental plant parts of shrubs from 0 40 C scored the highest points (7.100). Safeena et al. mid chills of Himachal Pradesh. J Ornamental Hort (2006) reported that good colour, appearance and texture 6(4):357-361 0 were recorded in the flowers dried at 40 C, since at this Dhatt K K, Singh K, Kumar R (2007) Studies on methods of temperature moisture is removed in a steady rate without dehydration of rose buds. J Ornamental Hort 10(4) affecting the structural integrity. Flower drying at higher 264-267 0 0 temperature of 50 C and 60 C significantly damaged Pandya HA, Saxena, OP (2001) Preservation of the quality parameters of the flowers and resulted in petal chrysanthemum species by drying. Acta Horticulture, fall. Bhutani (1999) observed that the flowers retained a International Society for Horticultural Science (ISHS). smooth texture when embedded in silica gel. Similar (543): 367-369 results were observed by Dhatt et al. (2007) who reported Peggy B (1978) Preserving flowers with silica gel. Information that embedding the rose buds in silica gel retains the Services, Canada. Department of Agriculture, Ottawa smooth petal texture of the flowers. Thus it can be K I A O C 7 concluded from the present investigation that silica gel Puri A (1995) Economic potential of dried flowers. Agricultural as a desiccant is the most suitable embedding material Marketing 36 (1): 43-46 for better acceptable flower quality like good appearance Safeena S A, Patil V S, Naik B H (2006) Response of drying and smooth texture of flower petals whereas copper in hot air oven on quality of rose flowers. J sulphate as a desiccant was the most suitable Ornamental Hort 9(2):114-117 embedding material for obtaining brighter flower. Singh A, Dhaduk B K (2005) Effect of dehydration techniques in some selected flowers. J Ornamental Hort 8 (2):155-156 v/;;u djus ds fy, xeZ gok vksou Qwyksa dk izn'kZu xq.koRrk esa lq/ Singh A, Dhaduk B K, Shah R R (2004) Effect of different kkj ds fy, ihys cVu izdkj xqynkmnh (Dendranthema grandiflora temperatures and embedding media on flower Tzvelv) ds lq[kkus ds fy, mi;qä desiccants ekufhdj.k ds fy, dehydration of zinnia (Zinnia linearis Beanth.). J Ornamental Hort 61 (3): 249-252 CRD desiccants vk;sftr dh xbZ A iz;ksx Hkkt; esa j[kh xbZ ik¡p Westland P (1992) The complete flower arranger. Annes vFkkZr~ ds lkFk] flfydk tsy] dSfY'k;e DyksjkbM] dkWij lYQsV] jsr Publishing Limited London 255 0 0 0 vkSj cksjsØl vkSj rhu lq[kkus rkieku vFkkZr 40 , 50 60 C & C (Manuscript Received : 17.06.2010; Accepted 21.08.2011) lw[ks Qwyksa dh lexz xq.koŸkk ds 5 ttksa ds ,d iSuy }kjk U;k; fd;k
174 JNKVV Res J 45(2): 175-180 (2011)
Morphological study of a watershed using Remote Sensing and GIS approach
S.K. Sharma, N.K. Seth, S. Tignath* and J.P. Shukla** Department of Soil and Water Engineering College of Agricultural Engineering Jawaharlal Nehru Krishi Vishwa Vidyalaya Jabalpur 482004 (MP) *Department of Geology Government Autonomous Science College Jabalpur (MP) **Advanced Materials and Processes Research Institute CSIR, Bhopal (MP)
Abstract scarcity region. The Gusuru river watershed is forested and hilly Water availability is considered to be one of the key factors watershed of the part of Satna and Panna district of for rapid development and urbanization. However, the over exploitation of water resources has resulted in a condition of Madhya Pradesh presents a case in point. According to unsustainability and environmental degradation. Each recent studies the region suffers a variable water crisis watershed has a number of distinctive characteristics, which with once perennial currents becoming seasonal or even govern the amount of precipitation received and runoff drying up entirely (Sharma 2010). In the region the produced. The morphometric analysis of a watershed plays problem is compounded by the fact that traditional an important role in deciding the hydrological behavior such agriculture activities relied long on substantial inputs from as runoff, soil erosion sediment delivery ratio etc. of a the surrounding forests in the form of fodder, wood for watershed. The Gusuru river watershed in Part of Panna and agricultural implements and litter for animal feeding. Satna district of Madhya Pradesh was considered for such a study. Remote Sensing data were used for the preparation of Together with the collection of fuel wood and grazing by updated drainage map. Various linear, aerial and relief animals in the forest these have led to severe parameters of the study area were determined using degradation of the remaining forestland. Thus, the classic Geographical Information System (GIS). It was observed that distinction between hydrologically benign forestland and the first order streams constitute 57.34 per cent of the total mismanaged agricultural/grazing land tends to become stream length whereas remaining 2nd , 3rd , 4th , 5th and 6th obscured. So, the scope of the problem is much wider order streams constitute 22.98 per cent, 6.81 per cent, 5.65 then the hydrological or ecological aspects. Therefore, per cent,, 3.54 per cent and 3.64 per cent respectively. such an approach should address the conservation and management of the physical (soil, water etc.) and Keywords: GIS, remote sensing, watershed, biological (forest, cattle, crops etc.) resources in the area geomorphology, morphometric analysis by ensuring people’s participation through taking their views and interests explicitly into account. Hence, information pertaining to hydrological behavior will be of Water is one of the most critical of natural resources. immense help. However, there is growing water crisis today, which is precipitating social and political turmoil in the society. The morphological and climatic characteristics of Increased human activity and growing requirements on a basin govern a hydrological response to a considerable the one hand and limitations in supply on the other hand, extent. The morphological characteristic of a basin have made it absolutely necessary to pay a closer represents its attributes, which may be employed in attention to all aspects of water resources. Assessing, synthesizing hydrological response. The importance of planning and managing water resources for sustainable morphological factors can not be overlooked in accurate use have become an important issue especially in water
175 prediction of runoff. Basin characteristics when unique technology and widened the spectrum of remote measured and expressed in quantified morphometric sensing application in natural resources management. parameters can be studied for the influence on runoff. Considering all these aspects an attempt has been made Hence, linking of the morphologic parameters with the to study the geomorphology of Gusuru river watershed hydrological characteristics of the basin can lead to with the help of remote sensing and GIS approach. simple and useful procedure to simulate the hydrologic behavior of various basins, particularly the ungauged Study area one. Interpretation and quantitative analysis of various drainage parameters enables qualitative evaluation of surface runoff, infiltration and susceptibility to erosion The study area Gusuru river watershed lies between within the basin. Chalm et al. (1996) have established latitude 240 6’ 32.75” and 240 16’ 24.07” N and longitude the relationships between hydrologic and 800 32’ 50.23” E and 800 37’ 31.14” E in the part of Panna geomorphological variables using statistical methods. and Satna district of Madhya Pradesh (Fig 1). The maximum and minimum elevation encountered in the An attempt has been made by Singh (1994) to watershed is 628 m and 339 m above mean sea level incorporate remote sensing for the evaluation of some respectively. The watershed encompasses an area of of the morphohydrological characteristics of Jojri basin. 155 km2. Temperature ranges between minimum of 40 Sharma et al (2010) have done morphometric analysis 0C during December or January months and the of Kanahiya Nala watershed using GIS package Arc GIS maximum of 45 0C in May or June. Average annual 9.3 and observed that the methodology is very efficient precipitation is 1100 mm and is concentrated mostly for estimation of geomorphological parameters. Studies between mid June to mid September with scattered conducted by Sharda et al. (1993) and Prasad et al. winter rains during late December and January months. (1997) reveled that remote sensing and GIS techniques are of great use in characterization and prioritization of watershed area. Morphometric Parameters Remote sensing has emerged as a powerful tool in recent past for natural resources assessment with the Various important morphometric parameters used in this ability of obtaining systematic, synoptic, rapid and study for analysis are described below. repetitive coverage. GIS has made remote sensing a Stream Order
Madhya Pradesh The most commonly used method for stream ordering (u) as suggested by Strahler (1964) was used for this study. The rules for stream ordering using this method are:
• Streams that originate at a source are defined to be first order streams. • When two streams of order u join a stream of order u+1 is created. • When two streams of different order join the channel segment immediately downstream has the higher of the order of the two joining streams. • The order of the basin is the order of the highest stream.
Stream number (Nu)
Stream number is the number of stream segments of various orders. It is inversely proportional to the stream Fig. 1 Location Map of Study area order.
176 Bifurcation ratio soil materials with dense vegetation and low relief, where as high drainage density is prevalent in regions of weak, impermeable sub surface materials which are sparsely Bifurcation ratio (R ) is the ratio of the number of streams b vegetated and have high relief (Strahler 1964). of given order u to the higher order u+1.
Nu Stream frequency (Sf) Rb= ——— (1)
Nu+1 Stream frequency is the number of streams per unit area of the basin. It mainly depends upon the lithology of the R characteristically ranges from 3.0 to 6.0 for b basin and reflects the texture of the drainage network. watersheds in which the geological structure does not distort the drainage pattern. Abnormally high bifurcation ratio might be expected in region of steeply dipping rock Elongation Ratio (Re) strata. It is defined as the ratio between the diameter of a circle with the same area that of the basin to the maximum Total stream length (La) length of the basin, and is computed as Total stream length is the length of all the stream having 2 A R e order u. The total stream length divided by the number L b of stream segments of the order gives the mean stream length for that order. The elongation ratio ranges from 0.6 to 1.0 over a wide variety of climatic and geological environments. Values Watershed perimeter (P ) r nearing 1.0 are typical of regions of low relief, where as values in the range of 0.6 to 0.8 are generally associated Watershed perimeter is the length of the watershed with strong relief and steep ground slopes. boundary.
Circulatory Ratio (Rc)
Maximum length of watershed (Lb) It is the ratio of the watershed area and the area of circle
Maximum length of watershed is the distance between of watershed perimeter (Pr). Circular basins with low watershed outlet and the farthest point in the watershed. bifurcation ratio produce a sharp peak. It is computed as 12.57 A
Form factor (Rf) Rc = ————— (4) 2 Pr Form factor is the ratio of the basin area (A) to the square of the maximum length of the basin (Lb). Maximum Watershed Relief (H) A It is the maximum vertical distance between the lowest Rf = ——— (2) 2 and highest points of a watershed. It is also known as L b total relief.
Drainage density (Dd) Relief Ratio (Rh) Drainage density is defined as the total length of the streams of all the orders of a basin to the area of the It is the total relief of watershed divided by the maximum basin. The drainage density gives an idea of the physical length of the watershed. It is an indicator of the potential properties of the underlying rocks. Low drainage density energy available to move water and sediment down the occurs in regions of highly resistant and permeable sub slope.
177 Ruggedness Number (RN) delineated using the SOI toposheet on 1:50000 scale and remote sensing data. It is defined as the product of the maximum watershed The delineated watershed boundary was further relief and its drainage density. It gives an idea of overall subdivided into the micro-watersheds and then micro- roughness of a watershed, and is computed as watersheds boundary along the drainage network was selected for morphometric analysis. The input R = H x D (5) parameters for morphological studies such as area, N d perimeter, elevation, stream length etc. were obtained directly in Arc GIS software using query based algorithm.
Relative Relief (Rr) Other morphological parameters were calculated using formulae based on input values. It is the ratio of the maximum watershed relief to the ERDAS IMAGINE 9.1 image processing software perimeter of the watershed. was used for image processing operation and Arc GIS 9.3 software was used for GIS analysis. For morphometric analysis area, perimeter, maximum length of basin, drainage map, stream length Result and Discussion of each order, numbers of streams of each order and watershed relief values are required. These inputs were derived by using GIS software. Once these inputs were The drainage ordering map based on Strahler (1964) th obtained, then by making use of the mathematical suggests that the Gusuru river watershed is a 6 order st formulae as discussed above, all the necessary stream (Fig 2). The GIS analysis shows that 1 order nd rd parameters for morphometric analysis were calculated. streams are 748, 2 order streams are 190, 3 order streams are 42, 4th order streams are 12 and 5th order streams are 4 in numbers. Materials and methods The total stream length of the different order reveals that the total stream length in the study is 530.754 Base map of the study area was prepared using the km. Out of the total streams length; first order streams Survey of India (SOI) toposheets no 63D/11, 63D/12 and constitute 57.34 per cent of the total stream length 63D/16. Remote sensing data (toposheet basis) of IRS- whereas remaining 2nd, 3rd, 4th, 5th and 6th order streams P6 LISS-III of the study area acquired on 25-10-2007 constitute 22.98 per cent, 6.81 per cent, 5.65 per cent,, 3.54 per cent and 3.64 per cent respectively. The delineated watershed was further divided into 14 micro-watersheds. All the computed morphometric are presented in Table 1 and 2. It can be seen that relief ratio value ranges from 0.019 to 0.046 for micro- watershed 1 and 9 & 14 respectively. High value of relief is an indication of quick depletion of water which results in large peak and steep limb hydrograph and higher soil loss. The relative relief values ranges from 0.006 for micro-watershed 1 to 0.017 for micro-watershed 9. The micro-watershed 14 has maximum ruggedness number i.e. 1.134 while micro-watershed 1 Fig 2 Drainage map of study area has the minimum value of RN i.e. 0.304. The micro- watershed 14 has overall high roughness. were procured. The geocoded satellite data was The elongation ratio value ranges between 0.634 for procured from National Remote Sensing Agency, micro-watershed 13 to 0.868 for micro-watershed 8. Hydrabad. Digital image processing techniques were Higher value of elongation ratio for micro-watershed 8 applied on the images, making use of spatial and indicates mature to old stage topography. According to radiometric enhancement techniques in order to remove elongation ratio index, micro-watershed 11 and 12 have shadow and for proper tone and texture of the image. an oval shape, micro-watershed 1,3,4,5 and 10 have Then the watershed boundary of the study area was less elongated shape and micro-watershed 2, 6, 13 and
178 Table 2 Micro-watersheds wise Morphometric Parameters
Micro Area (km2) Perimeter Elevation (m) Total No. of Max length Total watershed (km) Relief (m) Streams of watershed stream No. Max. Min. (km) length (km) 1 11.813 15.095 628.00 538.00 90.00 74 4.72 39.83 2 10.706 15.438 608.00 479.00 129.00 66 5.61 35.25 3 11.134 15.626 606.00 478.00 128.00 59 5.07 35.61 4 10.506 14.205 568.00 418.00 150.00 70 4.63 34.95 5 14.472 18.500 609.00 417.00 192.00 116 5.91 50.47 6 12.574 16.787 605.00 478.00 127.00 48 5.83 30.85 7 7.895 13.294 609.00 477.00 132.00 45 4.09 25.10 8 14.723 17.826 566.00 358.00 208.00 108 4.99 54.03 9 10.185 14.237 606.00 358.00 248.00 64 5.35 33.95 10 11.580 17.155 613.00 478.00 135.00 86 5.65 39.61 11 11.076 15.151 584.00 358.00 226.00 62 4.84 36.38 12 11.487 13.801 564.00 358.00 206.00 72 4.73 38.12 13 7.785 13.802 547.00 358.00 189.00 57 4.97 30.35 14 9.249 15.414 566.00 339.00 227.00 72 4.93 46.19
Table 3 Micro-watersheds wise Computed Morphometric Parameter
Micro Sf Dd Rr Rh RN Rf Re Rc Rb watershed 1. 6.264 3.372 0.006 0.019 0.304 0.530 0.822 0.651 3.889 2. 6.165 3.293 0.008 0.023 0.425 0.340 0.658 0.564 4.115 3. 5.299 3.199 0.008 0.025 0.409 0.433 0.743 0.573 3.521 4. 6.663 3.328 0.011 0.032 0.499 0.490 0.790 0.654 3.833 5. 8.016 3.488 0.010 0.032 0.670 0.414 0.726 0.531 3.646 6. 3.817 2.454 0.008 0.022 0.312 0.370 0.686 0.560 3.643 7. 5.700 3.180 0.010 0.032 0.420 0.472 0.775 0.561 3.417 8. 7.335 3.670 0.012 0.042 0.763 0.591 0.868 0.582 3.681 9. 6.284 3.334 0.017 0.046 0.827 0.356 0.673 0.631 3.705 10. 7.426 3.421 0.008 0.024 0.462 0.363 0.680 0.494 4.005 11. 5.598 3.285 0.015 0.047 0.742 0.473 0.776 0.606 3.208 12. 6.268 3.319 0.015 0.044 0.684 0.513 0.809 0.758 3.113 13. 7.322 3.899 0.014 0.038 0.737 0.315 0.634 0.513 3.495 14. 7.785 4.994 0.015 0.046 1.134 0.381 0.696 0.489 3.759
14 have elongated shape. 2 indicates a high runoff, less infiltration and mature The circular ratio values ranges from 0.489 nature of topography, which is the resultant of variation (minimum) for micro-watershed 14 to 0.758 (maximum) in higher and lower order segments. This reflects for micro-watershed 12. High value of circular ratio geologic and tectonic characteristics of area. correlates to mature and old stage of topography. The stream frequency ranges between 3.817 for Micro-watersheds having circular to oval shape micro-watershed 6 to 8.016 for micro-watershed 5. The allow quick disposal of runoff, and result in high peaked drainage density ranges from 2.454 (minimum) for micro- and narrow hydrograph; while elongated shape of micro- watershed 6 to 4.994 (maximum) for micro-watershed watersheds allows slow disposal of water, and result in 14. Low value of drainage density for micro-watershed broad and low peaked hydrograph. 6 indicates that it has highly resistant and impermeable sub soil materials with dense vegetation cover and low The bifurcation ratio values ranges from 3.113 relief, whereas high value for micro-watershed 14 (minimum) for micro-watershed 12 to 4.115 (maximum) indicates a situation conducive for quick disposal of runoff for micro-watershed 2. Higher value for micro-watershed and region of weak sub surface materials, high relief
179 and sparse vegetation. LVªhe] 3.54 izfr’kr ikapok vkMZj LVªhe ,oa 3.64 izfr’kr NVoka vkMZj LVªhe gSA Conclusion References The conventional method of morphometric analysis is time consuming, tedious and error prone. Integration of Chalam B N S Krishnaveni M and Karmegam M (1996) remote sensing and GIS allows reliable, accurate and Correlation analysis of runoff with geomorphic updated database on morphometric parameters of parameters. J Applied Hydrology 11(3& 4): 24-31 watershed. The morphometric analysis of different micro- Prasad B Honda S K and Murai S K (1997) Subwatershed watersheds within a watershed shows their relative Prioritization for Watershed Management Using characteristics with respect to hydrologic response. Remote Sensing and GIS. GIS Development (ACRS) Sharda D K Ravi M Venkatatatnam V L and Rao M O (1993) Watershed prioritization for soil conservation- A GIS ikuh dh miyC/krk dks ,d rsth ls fodkl vkSj 'kgjhdj.k ds fy;s approach. Geo Carto International (1): 27-34 egRoiw.kZ djdkas esa ls ,d ekuk x;k gSA gkykafd] ty lalk/kuksa ds Sharma S K (2010) Geomorphological based identification of nksgu ij unsustainability vkSj Ik;kZoj.k {kj.k dh 'krZ ij gqbZ gSA critical watershed for soil coservation using GIS and remote sensing. Unpublished Ph.D.Thesis J N K V izR;sd okVjlsM dh fo’ks"krk vyx&vyx gksrh gS tks fd o"kkZ dh V Jabalpur ek=k vksj mlesa gksus okys viokg dks fu;af=r djrh gSA ekjQksesfVªd Sharma S K Rajput G S Tignath S and Pandey R P (2010) v/;;u fdlh okVjlsM esa viokg Hkw&{kj.k] lsfMesaV fMysojh jsfl;ks Morphometric analysis and prioritization of a watershed using GIS. J I Water Resources Soc 30 vkfn gkbMªksyksftdy fcgsfo;j dks fuf’pr djus ,d egRoiw.kZ Hkwfedk (2):33-39 fuHkkrk gSA ;g v/;;u e/;izns’k ds iUuk ,oa lruk ftys dh xql: Singh S (1994) Remote sensing in the evaluation of morpho- hydrological characteristics of the drainage basines unh okVjlsM esa fd;k x;k gSA fjeksV lsaflax MsVk dk mi;ksx viMsVsM of the Jori catchment Annals of Arid Zone 33 (4): Mªsust eSi ds fy;s fd;k x;k gSA th-vkbZ- ,l- i)fr }kjk v/;;u 273-278 {ks= ds fofHkUu yhfu;j] ,fj;y vkSj fjfyQ iSjkehVj dks fudkyus Strahler A N (1964) Quantitative geomorphology of drainage basins and channel networks Handbook of Applied ds fy;s fd;k x;k gSA ;g ns[kk x;k gS fd LVªhe dh dqy yackbZ dk Hydrology: Ed By Ven Te Chow McGraw Hill Book 57.34 izfr’kr izFke vkMZj LVªhe] 22.98 izfr’kr f}rh; vkMZj Company New York 55-67 (Manuscript Received: 30.05.2010; Accepted: 30.03.2011) LVªhe] 6.81 izfr’kr r`rh; vkMZj LVªhe] 5.65 izfr’kr prqFkZ vkMZj
180 JNKVV Res J 45(2): 181-187 (2011)
Geomorphometric study of Gusuru river watershed using Remote Sensing & GIS technique
S.K. Sharma, N.K. Seth and S. Tignath* Department of Soil & Water Engineering College of Agricultural Engineering Jawaharlal Nehru Krishi Vishwa Vidyalaya Jabalpur 482004 (MP) *Department of Geology Government Autonomous Science College Jabalpur (MP)
salinity, alkalinity and water logging. The problem has to Abstract be looked upon prudently and a strategic approach has to be followed to mitigate the severity of the problem. Geomorphometric study of a watershed plays a very important role in assessing the hydrological behavior of the ungauged watersheds or in inadequate data situations. Therefore, in this For the efficient and sustainable management of study morphometric analysis and prioritization of forteen micro- land and water resources one has to look for the watersheds of Gusuru river watershed, which is a tributary of sustainable unit. So that these resources can be handled Tons river considered for this study. The morphometric parameters considered for the analysis are stream order , and managed effectively, collectively and simultaneously. stream length, , stream frequency, bifurcation ratio, form factor, A watershed is an ideal unit for management of these circulatory ratio, elongation ratio, drainage density, natural resources for mitigation of the impact of natural ruggedness number, length of overland flow, relative relief, disasters for achieving sustainable development. An relief ratio, average slope and compactness coefficient all accurate understanding of the hydrological behavior of these parameters are determined in Remote Sensing & GIS watershed is important for the effective management. environment. After analysis of morphometric parameters Intensive study of individual watershed is therefore compound parameter values are calculated and prioritization rating of forteen micro- watersheds is carried out. The micro- necessary for developing management plan, which watershed 9 has lowest compound parameter value i.e 5.07 require immense data. In India most of the watershed is is likely to be subjected to a maximum soil erosion; hence it ungauged one. So, the Geomorphometric analysis of requires immediate attention for provisions of soil conservation watershed can play an important role in inadequate data measures. situations. Morphometric characteristics of represents its attributes and can be helpful in synthesizing its Keywords: GIS, water shed and Prioritization hydrological behavior (Pandey et al. 2004). Integrated use of remote sensing and GIS Land degradation has become a global issue due to soil techniques can be used in soil erosion assessment and erosion by various agents/process is highly alarming. studies on prioritization of sub watersheds for India possesses the highest percent of cultivated area development. The input parameters required for soil (about 46 %) in the world but its crop productivity is erosion modeling can be generated by remote sensing. invariably very low in comparison to other agriculturally Geographical information system helps in creation of advanced countries. According to an estimate 175 Mha database for the watershed which is very much useful of land in India constituting about 53 percent of the for carrying out spatial analysis there by helping the geographical area ( 329 Mha), suffers from deleterious decision makers in framing appropriate measures for effect of soil erosion and other forms of land degradation. critically affected area. Sediment yield and soil erosion Active erosion caused by water and wind alone account studies using GIS and remote sensing technologies have for 150 Mha of land, which amounts to loss of 5.3 MT of been carried out by many investigators. Desmukh et al. subsoil per year. In addition remaining 25 Mha land have (2007) used GIS technique for automatically calculating been degraded due to ravine, gullies, shifting cultivation, the MUSLE parameters for Bandha catchment of upper
181 Damoder valley in Jharkhand state. Materials and methods Studies conducted by Sharda et al. (1993), Prasad et al. (1997) and Sharma et al. (2007) reveled that remote In the present study morphometric analysis and sensing and GIS techniques are of great use in prioritization of micro-watersheds of Gusuru river characterization and prioritization of watershed area. watershed is carried out based on the integrated use of Morphometric analysis could be used for prioritization remote sensing and GIS techniques. Base map of the of micro watersheds by studying different linear and aerial study area was prepared using Survey of India (SOI) parameters of the watershed even without the availability toposheets 63 D/11, 63 D/12 and of soil maps (Biswas et al. 1999). Morphometric analysis 63 D/16 on 1:50000 scale. IRS P6 LISS III satellite requires measurement of linear features gradient of imagery with 23.5 m spatial resolution acquired on 25th channel network and contributing ground slopes of the October 2007 with Row-100 and Path-55 was used as drainage basin. Thakker et al. (2007) carried out study source data. The remotely sensed data was on morphometric analysis and prioritization of geometrically corrected and resampled taking miniwatershed in Mohr watershed, Gujrat using remote toposheets as reference. Image enhancement sensing and GIS techniques. In the present study techniques were applied to extract the drainage layer morphometric analysis and prioritization of micro- from FCC (False Colour Composite) for better watersheds are carried out for forteen micro-watersheds interpretation of the stream order. The digitization of of Gusuru river watershed of Satna and Panna district, drainage was carried out using ARC/INFO tools. The Madhya Pradesh by using RS and GIS techniques. stream ordering was carried out using Strahler (1957) stream ordering technique. The study area was divided Study Area into micro-watersheds. The fundamental parameters namely; stream length, area, perimeter, number of streams, and basin length were derived in GIS Gusuru river watershed is located in Part of Panna and environment. The morphometric parameters for the Satna district of Madhya Pradesh and covers an area of delineated watershed area were calculated using formula 155 km2 and is bounded between 800 32’ 50.23” E and suggested by Horton (1945), Strahler (1964), Schumn 800 37’ 31.14” E longitude, 240 6’ 32.75” and 240 16’ (1956) and Miller (1953) given in Table 1. 24.07” N latitude (Fig 1). The maximum and minimum elevation encountered in the watershed is 628 m and The morphometric parameters values namely; 339 m above mean sea level respectively. stream order , stream length , stream frequency, bifurcation ratio, form factor, circulatory ratio, elongation Madhya Pradesh ratio, drainage density, ruggedness number, length of overland flow, relative relief, relief ratio, average slope and compactness coefficient calculated using above formulae. Prioritization rating of all the micro-watersheds of Gusuru river watershed was carried out by calculating the compound parameter values. The micro-watershed with lowest compound parameter value was given the highest priority.
Result and Discussion
The drainage and micro-watershed map of Gusuru river watershed is presented in Fig 2. After analysis of the drainage map, it was found that the Gusuru river watershed is of 6th order type. The fundamental parameters namely; stream length, area, perimeter, basin length, and number of stream of each of the micro- watersheds are shown in Table 2. Computed morphometric parameters are presented in Table 3.
Fig. 1 Location Map of Study area Lower Rb values are the characteristics of structurally less disturbed watershed without any
182 Table 1. Formula for Computation of Morphometric Parameter
Morphometric Parameters Formula Reference
Stream order (u) Hierarchical rank Strahler (1964)
Stream Length (Lu) Length of the stream Horton (1945)
Mean Stream Length (Lsm) (Lsm) = Lu / Nu
Where, Lsm = Mean Stream Length
Lu = Total stream length of order u
Nu = Total number of stream segment of order u Strahler (1964)
Bifurcation Ratio (Rb) Rb = Nu/ Nu+1
Where, Rb = Bifurcation Ratio
Nu = Total number of stream of segment of order u
Nu+1 = Total number of stream of segment of next higher order Schumn (1956)
Mean Bifurcation Ratio (Rbm) Rbm = Average of bifurcation ratio of all order Strahler (1964
Basin length (Lb) Lb = 1.312 * A0.568
Where, Lb = Length of basin (km) A = Area of basin (km2) Nookaratnam et al. (2005)
Drainage density (Dd) Dd = Lu/ A
Where, Dd = Drainage density
Lu = Total stream length of order u A = Area of basin (km2) Horton (1945)
Stream Frequency (Fs) Fs = Nu/ A
Where, Nu = Total number of streams of all order A = Area of basin (km2) Horton (1945) Texture ratio (T) T = N / P Where, T = Texture ratio
N1 = Total number of streams of all order P = Perimeter (km) Horton (1945) 2 Form factor (Rf) Rf = A/ Lb
Where, Rf = Form factor A = Area of basin (km2)
Lb = Length of basin (km) Horton (1945)
Circulatory ratio (Rc) Rc = 4 A / P2
Where, Rc = Circulatory ratio A = Area of basin (km2) P = Perimeter (km) Miller (1953) 0.5 Elongation ratio (Re) Re = (2/Lb) * (A/ ) Schumn (1956) 0.5 Compactness coefficient (Cc) Cc = 0.2821 P/A
Where, Cc = Compactness coefficient P = Perimeter (km) A = Area of basin (km2) Horton (1945)
Ruggedness Number RN = H × Dd
Where, RN = Ruggedness Number H = Maximum watershed relief (m)
Dd =Drainage density
Relief ratio Rh = H × Ldb
Where, Rh = Relief ratio H = Maximum watershed relief (m)
Lb = Length of the basin
Relative relief Rr = Rh × P
Where, Rr = Relative relief
Rh = Relief ratio P= Perimeter of the basin
Length of Over land flow Lo = 1/ 2 Dd
Where, Lo = Length of Over land flow
Dd =Drainage density HL S ca Average Slope of watershed a 10 A
Where, Sa = Average Slope of watershed H = Maximum watershed relief
Lca = Length of the Contour A = Area of Watershed
183 distortion in drainage pattern (Nag 1998). The micro- watershed 2 has maximum (Rb = 4.115) while micro- watershed 12 has minimum Rb (Rb = 3.113). The value of Rb for micro-watersheds shows that influence of geological structure on the drainage network is negligible.
The micro-watershed 8 has maximum value (Rf = 0.591) while micro-watershed 13 has minimum value of Rf (Rf = 0.315). Low value of farm factor indicates elongated shape of micro-watersheds.
The micro-watershed 8 has maximum value (Re = 0.868) while micro-watershed 13 has minimum value of Re (Re = 0.634). The range of Elongation ratio values of micro-watersheds show that these micro-watersheds Fig 2 Micro-watershed boundary and drainage are elongated in shape.
The micro-watershed 12 has maximum value (Rc =3.817). The relief ratio value ranges from 0.019 to 0.046 = 0.758) while micro-watershed 14 has minimum value for micro-watershed 1 and 9 & 14. respectively. The
Rc (Rc = 0.489). According to Miller range micro- micro-watershed 14 has maximum ruggedness number watersheds are elongated in shape have low discharge (RN = 1.134) while micro-watershed 1 has the minimum of runoff and high permeability subsoil condition. value of RN (RN = 0.304). The micro-watershed 14 has overall high roughness. The micro-watershed 8 has The micro-watershed 14 has maximum value of (D = d maximum (T = 6.058) while micro-watershed 6 has 4.994) while micro-watershed 6 has minimum D (D = d d minimum T (T = 2.859). The micro-watershed 5 has 2.454). Low value of Dd for micro-watershed 6 indicates maximum value (Cc = 2.111) while micro-watershed 1 that it has the region underlain by highly permeable has minimum value of C (C = 1.329). subsoil material with dense vegetative cover and low c c relief. Micro-watershed 14 has high value of Dd indicates Micro-watershed 6 has maximum (Lo= 0.204 km) well developed network and torrential runoff result and micro-watershed 14 has minimum (Lo= 0.100 km) intense flood, whereas, high Dd value also indicate a length of overland flow among fourteen micro- situation conductive for quick disposal of runoff, region watersheds. The average slope of micro-watershed of weak subsurface material, high relief and sparse varies from 7.089 per cent for micro-watershed 1 to vegetation (Nautiyal 1994). 22.295 per cent for micro-watershed 12.
The micro-watershed 5 has maximum (Fs = The resource considerations for implementation
8.016), while micro-watershed 6 has minimum Fs (Fs of watershed management programme for various other
Table 2 Micro-watersheds wise Morphometric Parameters
Micro Area (km2) Perimeter Elevation (m) Total No. of Max length Total watershed (km) Relief (m) Streams of watershed stream No. Max. Min. (km) length (km) 1 11.813 15.095 628.00 538.00 90.000 74 4.720 39.836 2 10.706 15.438 608.00 479.00 129.000 66 5.610 35.257 3 11.134 15.626 606.00 478.00 128.000 59 5.070 35.614 4 10.506 14.205 568.00 418.00 150.000 70 4.630 34.959 5 14.472 18.500 609.00 417.00 192.000 116 5.910 50.471 6 12.574 16.787 605.00 478.00 127.000 48 5.830 30.859 7 7.895 13.294 609.00 477.00 132.000 45 4.090 25.105 8 14.723 17.826 566.00 358.00 208.000 108 4.990 54.032 9 10.185 14.237 606.00 358.00 248.000 64 5.350 33.953 10 11.580 17.155 613.00 478.00 135.000 86 5.650 39.613 11 11.076 15.151 584.00 358.00 226.000 62 4.840 36.386 12 11.487 13.801 564.00 358.00 206.000 72 4.730 38.126 13 7.785 13.802 547.00 358.00 189.000 57 4.970 30.351 14 9.249 15.414 566.00 339.00 227.000 72 4.930 46.192
184 reasons pertaining to administrative or even political was given a rating of 2, and so on as the consideration may limit the implementation to few micro- geomorphological parameters generally show positive watersheds. Even otherwise, it is always better to start correlation with soil erosion (Biswas et al 2002; Nooka management measure from the highest priority micro- Ratnam et al 2005; Thakkar and Dhiman 2007). The watershed, which makes it mandatory to prioritize the lowest value was rated last in the series number. micro-watersheds available. Watershed prioritization is For the basin shape parameters i.e. form factor, thus ranking of different micro-watershed according to circulatory ratio elongation ratio and compactness order in which they have to be taken for treatment and soil conservation measures. Hence, it was necessary to coefficient the lowest value was given a rating of 1, the next lowest value was given a rating of 2, and so on as evolve suitable mechanism for prioritizing the micro- the basin shape parameters generally show negative watersheds. correlation with soil erosion (Biswas et al 2002; Nooka For the prioritization of micro-watersheds ranking Ratnam et al 2005; Thakkar and Dhiman 2007). of micro-watersheds was determined. The highest value of geomorphological parameters i.e. stream frequency, Table 4 presents the ranking and final prioritization length of overland flow, drainage density, texture ratio, of micro-watersheds based on geomorphometric relative relief, relief ratio, average slope of watershed parameters. and ruggedness number among forteen micro- To facilitate the phase wise implementation, all the watersheds was given rating of 1, the next highest value
Table 3 Micro-watersheds wise Computed Morphometric Parameter Micro Fs Lo Dd T Rr Rh Sa RN Rf Re Cc Rc Rb watershed 1. 6.264 0.148 3.372 4.902 0.006 0.019 7.089 0.304 0.530 0.822 1.239 0.651 3.889 2. 6.165 0.152 3.293 4.275 0.008 0.023 9.275 0.425 0.340 0.658 1.331 0.564 4.115 3. 5.299 0.156 3.199 3.776 0.008 0.025 8.121 0.409 0.433 0.743 1.321 0.573 3.521 4. 6.663 0.150 3.328 4.928 0.011 0.032 13.524 0.499 0.490 0.790 1.236 0.654 3.833 5. 8.016 0.143 3.488 6.270 0.010 0.032 12.890 0.670 0.414 0.726 1.372 0.531 3.646 6. 3.817 0.204 2.454 2.859 0.008 0.022 7.467 0.312 0.370 0.686 1.335 0.560 3.643 7. 5.700 0.157 3.180 3.385 0.010 0.032 8.680 0.420 0.472 0.775 1.335 0.561 3.417 8. 7.335 0.136 3.670 6.058 0.012 0.042 20.115 0.763 0.591 0.868 1.311 0.582 3.681 9. 6.284 0.150 3.334 4.495 0.017 0.046 17.845 0.827 0.356 0.673 1.259 0.631 3.705 10. 7.426 0.146 3.421 5.013 0.008 0.024 9.998 0.462 0.363 0.680 1.422 0.494 4.005 11. 5.598 0.152 3.285 4.092 0.015 0.047 14.566 0.742 0.473 0.776 1.284 0.606 3.208 12. 6.268 0.151 3.319 5.217 0.015 0.044 22.295 0.684 0.513 0.809 1.149 0.758 3.113 13. 7.322 0.128 3.899 4.130 0.014 0.038 20.416 0.737 0.315 0.634 1.395 0.513 3.495 14. 7.785 0.100 4.994 4.671 0.015 0.046 11.553 1.134 0.381 0.696 1.430 0.489 3.759
Table 4 Prioritization of Micro-watersheds Micro- Fs Lo Dd T Rr Rh Sa RN Rf Re Cc Rc Rb Compound Final watershed parameter priority 1 9 9 6 6 14 14 14 14 13 13 3 12 3 10.00 14 2 10 5 10 9 10 12 10 10 2 2 8 7 1 7.38 8 3 13 3 12 12 11 10 12 12 8 8 7 8 10 9.69 12 4 6 7 8 5 7 8 6 8 11 11 2 13 4 7.38 8 5 1 11 4 1 8 7 7 7 7 7 11 4 8 6.38 4 6 14 1 14 14 13 13 13 13 5 5 10 5 9 9.92 13 7 11 2 13 13 9 9 11 11 9 9 9 6 12 9.53 11 8 4 12 3 2 6 5 3 3 14 14 6 9 7 6.76 5 9 7 8 7 8 1 2 4 2 3 3 4 11 6 5.07 1 10 3 10 5 4 12 11 9 9 4 4 13 2 2 6.76 5 11 12 4 11 11 3 1 5 4 10 10 5 10 13 7.61 10 12 8 6 9 3 2 4 1 6 12 12 1 14 14 7.07 7 13 5 13 2 10 5 6 2 5 1 1 12 3 11 5.84 3 14 2 14 1 7 4 3 8 1 6 6 14 1 5 5.53 2
185 micro-watersheds are prioritized on the basis of esa lcls T;knk Hkw{kj.k gksxkA blfy;s bl ekbØks okVj lsM esa Hkw morphometric analysis. The compound parameter laj{k.k mik;ksa dks loZizFke izkjaHk djus dh t:jr gSA values of forteen micro-watersheds of Gusuru river are calculated and prioritization rating is shown in Table 4. The micro-watershed 9 with a compound parameter References value of 5.07 receives the highest priority (one) with the next in the priority is micro-watershed 14 and having the Biswas S Sudhakar S and Desai V R (1999) Prioritization of compound parameter value of 5.53. Highest priority Subwatershed Based on Morphometric Analysis of indicates the greater degree of erosion in the particular Drainage Basins - A Remote Sensing and GIS micro-watershed and it becomes potential candidate for Approach. J Indian Soc Remote Sens 27(3):155- applying soil conservation measures. Thus, soil 166 conservation measures can first be applied to micro- Biswas S Sudhakar S and Desai V R (2002) Remote Sensing watershed 9 and then to other depending on their priority. and geographic Information system Based Approach for watershed conservation. J Surveying Engineering 108-124 Conclusion Desmukh S B Pandey V K and Jain M K (2007) Integration of GIS with MUSLE in assessment of sediment yield. Indian Jour of Soil Cons 35(1): 1-5 The quantitative morphometric analysis was carried out in forteen micro-watersheds of Gusuru river watershed, Horton R E (1945) Erosional development of streams and their drainage basin hydrological approach to using RS & GIS technique for determining the linear, quantitative morphology. Geol Soc Am Bull 56: 275- aerial and relief aspects of micro-watersheds. The 370 conventional methods of morphometric analysis are time Miller V C (1953) A quantitative geomorphic study of drainage consuming, tiresome and error prone, while use of RS basin characteristics in the Clinch mountain area & GIS technique allows for more reliable and accurate Virginia and Tennesses Deptt of Navy Office of Naval estimation of similar parameters of watersheds. The Res. Technical Report 3 Project NR 389-042 morphometric analysis of different sub watersheds Washington DC shows their relative characteristics with respect to Nag S K (1998) Morphometric Analysis Using Remote Sensing hydrologic response of the watershed. The results of Techniques in the Chaka Sub-Basin Purvlia District morphometric analysis show that micro-watershed 9 and West Bengal. J Indian Soc Remote Sens 26(1-2) : 14 are possibly having high erosion. Hence, suitable soil 69-76 erosion control measures are required in these micro- Nautiyal M D (1994) Morphometric analysis of a drainage basin using arial photographs : A case study of Khairkuli watersheds to preserve the land from further erosion. basin District Deharadun. J Indian Soc Remote Sens 22(4): 251-262 vuxstM okVj lsM ;k tgkWa ij gkbMªksyksftdy MsVk ukius dh lqfo/ Nooka Ratnam K (2005) Check Dam Positioning by Prioritization of Micro Watershed Using SYI Model kk miyC/k u gks] mu txgksa ds gkbMªksyksftdy fcgksfo;j dks le>us and Morphometric Analysis - Remote Sensing and ds fy;s T;kseksjQksesfVªd v/;;u ,d vge~ Hkwfedk j[krk gSA vr% GIS Perspective. J Indian Soc Remote Sens 33(1) : 25-38. ;g 'kks/k dk;Z VksUl unh dh milfjrk xql: unh ij fd;k x;k gSA Pandey A Chowdary V M and Mal B C (2004) Morphological ftlesa xql: unh ds pkSng ekbØksokVj lsM dh ekjQksesfVªd fo’ys"k.k Study of Watershed Using Geographical Information ds mijkar mudk izk;jVktslu fd;k x;k gSA bl v/;;u esa tks System. Proceeding of International Conference and Emerging Technologies in Agricultural and Food eksjQksesfVªd iSjkehVj fy;s x;s gSa os bl izdkj gSa& LVªhe vkMZj] LVªhe Engineering (etae. 2004) IIT Kharagpur yackbZ] LVªhe fQzDosalh] ckbQjdslu jsfl;ks] QkeZ QsDVj ljdqysVjh December11-17: 34-40 Prasad B Honda S K and Murai S K (1997) Subwatershed jsfl;ks] byksuxs’ku jsfl;ks] ,sojst Lyksi] Mªsust MsuflVh] jXxMusl Prioritization for Watershed Management Using uacj] ysaFk vkQ vksojysaM Qyks] fjysfVo fjyhQ] fjyhQ jsfl;ks] Remote Sensing and GIS. GIS Development (ACRS) ,sojst Lyksi vkSj dEisdVusl dksfQfl;sUVA ;s lHkh iSjkehVj fjeksV Schumm S A (1956) Evaluation of drainage system and slopes in bed lands at Perth Ambry New Jersy. Geolo Soc lsflax ,oa th-vkbZ-,l- }kjk fudkys x;s gSaA ekjQksesfVªd iSjkehVj Am Bull 67:597-646 fudkyus ds ckn daikmM iSjkehVj osY;w fudkyh x;h gSA mlds vk/ Sharda D K Ravi M Venkatatatnam V L and Rao M O (1993) kkj ij ekbdzksokVjlsM dk izk;jVkbts’ku fd;k x;k gSaA ekbdzks okVj Watershed prioritization for soil conservation- A GIS approach Geo Carto International (1): 27-34 lsM 9 dh daikmM iSjkehVj osY;w 5.07 gS vr% bl ekbØks okVj lsM
186 Sharma S K Mishra N and Gupta A (2007) Morphometric basins and channel networks Handbook of Applied analysis of Uttala nala watershed using GIS Hydrology: Ed By Ven Te Chow McGraw Hill Book techniques. Sci-fronts 1:178-185 Company New York 55-67 Strahler A N (1957) Quantitative analysis of watershed Thakkar A and Dhiman S D (2007) Morphometric analysis geomorphology. Trans Am Geophys Union 38:913- and prioritization of mini watersheds in Mohr 920 watershed Gujarat using remote sensing and GIS Strahler A N (1964) Quantitative geomorphology of drainage techniques. J Indian Soc Remote Sens 35 (4): 313- (Manuscript322 Received: 30.05.2011; Accepted: 17.07.2011)
187 JNKVV Res J 45(2): 188-191 (2011)
Effect of ratio (n/v) on optimizing clearance angle and weeding efficiency
Devvrat Singh and Jayant Negi* Directorate of Soybean Research Khandwa Road, Indore 452 001 (MP) *Acropolis Institute of Engineering, Indore (MP)
Abstract plants. Manually operated tools and tractor drawn sweep type weeders work on the principle of horizontal shearing of the soil and are limited to slicing the soil thus usually A study was conducted on clearance (d) angle as a design parameter of L shaped tractor PTO operated rotary tool fail to prevent re-establishment of the weed plants. In attachable to the tractor PTO weeding machine. The order to perform efficient weed management, an attempt parameter clearance angle (d) of the rotary tool was tested was made to develop a rotary weeding machine. The against the ratio of revolutions per minute (n) and forward basic principle in the working of the rotary weeding speed (v) for different lengths of tools during weeding operation machine is provision of centrifugal force acting on to with the rotary tool. Often the said tool fails to serve the purpose cut the soil, uproot and cut the weed plant, and throw as negative clearance angle at lower ratios of n/v fails to the cut slice of soil. This sequence is repeated eliminate the weed plant due to poor penetration of the tool in continuously. the soil which causes walking out of the tool from the soil. Therefore the clearance angle (d) was studied against the One of the important design parameter is range of n/v from of 2:1 to 14:1. It was evaluated to optimize clearance angle of the tool which depends upon rpm the clearance angle (d) as a design parameter. An equation and forward speed of the prime mover responsible for for rotary tool was developed and used to compute the pulling the machine and revolution per minute of the clearance angle (d) for optimum value for the tractor PTO operated rotary weeding tool. These theoretically optimized rotary tool. The direction of rotation of the tool is a basic results showed that optimum value of clearance angle (d) (5.25 rotary design parameter, and is used inline with the to 5.84 degree clearance angle) was on keeping the n/v ratio common practice in the country to use only the forward between 8:1 and 12:1. direction of rotation (the rotary tool follows the direction of travel of the tractor wheels). The field test revealed that for ratio of n/v from 8:1 to 12:1 led The present study was aimed at optimization of to 87- 90 per cent weed elimination and the clearance angle clearance angle (d) as design parameter of the tool to (d) remained in the range of 5.25 degree to 5.84 degree. optimize the weeding efficiency by tractor power take Results suggest that for optimization and efficient operation, off (PTO) operated rotary weeding tool. A mathematical the tool needs to be operated between the ranges of ratios of 8:1 to 12:1 to achieve maximum weed elimination. equation was developed for the clearance angle (d) as design parameter, which permits to decide optimum range of operation and performance can be decided and Keywords: clearance angle (d), optimization, power take selected immediately for production of rotary tools for off, ratio of RPM vs. forward speed, rotary tool specific soil and field conditions. Optimization of the design parameter of the tractor PTO operated tool is The removal of undesired plants is the most critical field helpful in designing and development of rotary weeding operation, after sowing of the crop that affects the final tool for different operating speeds, soil conditions and yields if not accomplished effectively. The yield loss due moisture conditions. to weed infestation in soybean has been reported to the In rotary tool, the tool is fixed at predetermined tune of 20-77 per cent (Muniyappa et al.1986, Tiwari and drilled holes on the flange, which is welded on the shaft Kurchania, 1990, Kurchania et al. 2001). Substantial yield of the rotary machine, thus having a control over cutting loss can be avoided by efficient management of weed angle. The transfer of power directly to soil by using
188 tractor rather than through inefficient drawbar has some thickness). Number of tools (z=3) per flange was taken inherent advantages of rotary or active powered tools for the study. for considering them as an alternative to tractive tools. Smaller clearance angle were formed when the Clearance angle (d): ratio of rpm and forward speed were low. The optimisation of clearance angle of weeding tool would This developed equation helps to select clearance angle enable to design of the tool to solve the problems (d) under suitable limits against n/v so that the tool cuts associated with the various types of weed elimination a slice with minimum of effort and does not leave slice situations. It would realise the true potential of the holding the weed plant. Thus, following assumptions mechanization of weeding methods. were taken into consideration for the development of the equation. Material and methods = - 1
The study comprised of (i) development of equation for clearance angle (d) of the L shaped tool, (ii) validation of the developed equation for clearance angle (d) as design parameter, and (iii) optimisation of clearance Where, H = Depth of soil, R= length of the tool, d= n/v ,n d as design parameter and with field angle ( ) = number of revolution of the tool, v = forward velocity of observations for three years. machine (cm/sec),z=number of tools Forward speed of the tool was controlled with the help of a gear selection with the ground PTO type Validation of the equation for clearance angle (d) of the tractors. The tool rpm was controlled with the help of tool PTO speed which was taken from independent PTO system of the tractor. The clearance angle (d) (Fig 1) was measured as and angle between tangent to the Validation was conducted by changing the values of n/ formed trochoid and the line passing through the v by changing values of n and keeping v constant and sharpness angle made by the tool / tools operated by calculating the clearance angle (d) cut by the tool. The the tractor power take off operated rotary machine. data was recorded on revolutions per minute and forward speed for 3 tools on the flange of the rotary shaft of the Study on the major design parameter of weeding tractor PTO driven machine. tool i. e. clearance angle (d) was conducted for different speed of rotary tool and the prime mover (n/v). The length The data of different values of n, v and z was put of the cutting tool was taken R = 30 cm and R = 28 cm in the equation to calculate the clearance angle (d) and which were mounted on a flange fixed on a shaft of the graph was drawn (Fig. 2). The result from data and the tool. The cutting tool was made of spring steel (5 mm graph clearly depict that with the increase in n/v the
Clearance angle
7 100 6 80 5 Clearance angle (d) 4 60 ß- ß1 = d (d) 3 40 Weed elimination 2 (%) 20 1 0 0 0 2 4 6 8 10 12 14 16 (n/v)
Fig. 1 Clearance angle ( ) vs n/v R =30 cm Fig. 2 Clearance angle ( ) vs n/v R =30 cm
189 clearance angle (d) increases and vice versa. Thus, the and after the operation of the tool and recorded with developed equation was found to be valid for different different forward speeds and revolutions per minute from ratios of n/v. Sakai and Hai (1982) reported that the 10 randomly selected places from ten replicated plots clearance angle at the untilled side surface of the (each plot- 50 m x 2.25 m) for each of the above two lengthwise blade is an important parameter for the design treatments. The bite length data from each plot was of rotary blade. recorded. The clearance angle (d) of the tool made with the Optimization of clearance angle (d) of the tool uncut soil was in the field test with weed elimination percentage. The data of weeds/m2 was recorded. The Optimum range of n/v for the clearance angle (d) was pattern followed by the change of ratio upon the length achieved with changing the n/v by changing the forward of cut was evaluated. Test was conducted with tool length speed of the machine which is operated with the prime R = 30 cm. Forward speed selected for the study was v mover (tractor). The equation of clearance angle was = 20 cm / seconds which was kept constant. This tested with the change of n/v for different values of n revolution per minute of the tool was varied from n = 20 and keeping v constant to find out clearance angle for onwards. The above said data was selected to vary the optimization with the change of revolutions per minute ratio of n/v and the clearance angle (d) of the tool was of the tool which respect to its forward speed. The recorded for the study. optimization of clearance angle was evaluated with the performance of weed count different lengths of tools. Results and Discussion The values of the clearance angle) of three sets were used in the equation developed for finding out the clearance angle to establish the optimum value or range The testing of the equation developed was accomplished of n/v. Field testing was conducted to collect data for to compute clearance angle (d), for different ratios of measurement of bite length against n/v and elimination rotary rpm and forward speed. The study clearly indicates of weeds. The tests were conducted for three sets for that the clearance angle (d) variation affects the change each tool. The data for the clearance angle (d) against in the percentage of weed elimination. Increase or different ratios n/v of the tractor power take off operated decrease in the length of tool on the same flange with rotary tool was analyzed. the same ratio of n/v was found to be directly proportional to the clearance angle (Table 1). The clearance angle (d) of the tool was computed after obtaining the sample from the field after the The ratio of n /v, 8:1 to 12:1 was in the range in operation of the different number of tools with various which the clearance angle (d) as per the developed ratios of n/v. The number of weeds were collected before
Table 1. Effect of ratio (n/v) on optimizing clearance angle ( ) and weeding efficiency
RPM (n) Forward Ratio of Length of tools R-30 cm Length of tools R-28 cm speed (v) (n/v) Predicted clearance Weed Predicted clearance Weed (cm/sec) angle ( ) (cm) elimination (%)* angle ( ) (cm) elimination (%)*
40 20 2 0.57 60.25 -1.36 21.00 60 20 3 2.13 82.30 1.63 68.00 80 20 4 3.56 83.00 3.04 82.00 100 20 5 4.16 85.80 3.87 84.00 120 20 6 4.65 86.50 4.41 85.50 140 20 7 4.99 87.50 4.71 86.50 160 20 8 5.25 87.70 5.07 87.00 180 20 9 5.45 88.30 5.12 87.50 200 20 10 5.61 88.80 5.46 88.50 220 20 11 5.73 89.50 5.61 89.50 240 20 12 5.84 90.00 5.72 90.00 260 20 13 5.93 90.50 5.82 91.50 280 20 14 6.01 91.50 5.86 92.00
*Mean of three years
190 equation revealed that with increase in the ratio of n/v, 8:1 to 12:1 was sufficient to uproot slash and shake away the clearance angle (d) increased proportionately. The around than 87 to 90 per cent of the weed plants from increase of clearance angle (d) with the increasing the the soil. Thus z=3 and the range of ratios of n/v 8:1 to ratio n/v when put in the equation proves the validity of 12:1 (Fig. 2) was considered to be optimum for the design the equation for the clearance angle (d). parameter of clearance angle (d). Ratio of n/v of 8:1 to 12:1 was optimum for Field test results clearance angle in the range of 5.25 degree to 5.84 degree. (d). At this range maximum weeding efficiency Study on weeding operation for first set was done with was the highest. the tool for clearance angle (d) computation theoretically and in the field, there was significant difference in the weed population. The ratio above of n/v 12:1 not much VªSDVj ihVhvks pfyr jksVjh fuankbZ e’khu }kjk pfyr Vwy dk variation /increase of weed elimination was observed. fDy;jsal dks.k fMtkbu iSjkehVj ds :Ik es v?;;u fd;k x;kA The result for z=3 showed that reduction in weed fDY;jsal dks.k ¼ ½ dks jksVjh Vwy ds pDdj izfrfefuV o vkxs fd population was 87 to 90 per cent when n/v was kept in the ratio from 8:1 to 12:1. xfr ds eqdkcys ijh{kr fd;k x;k n/v ds fupys vuqikr ds vuqlkj pyus ij fDy;jsal dks.k _.kkRed gksus dh otg ls jksVjh Vwy funkbZ Optimization of the clearance angle (d) as design djus ij vleFkZ gks tkrk gSaa vr% n/v dk v?;;u 2:1 ls 14:1 ds parameter cht fd;k x;k fDy;jsal dks.k dks fMtkbu iSjkehVj ds :Ik esa vuqdwfyr djus gsrq ewY;kadu fd;k x;k A jksVjh midj.k ds The data for optimization of clearance angle (d) in the fDy;jsal dks.k izkIr djus gsrq fodflr lHkhdj.k dk iz;ksx dj equation provide value of clearance angle (d) which can n/v 8:1 12:1 draw the curves for number of tools (z = 3) on the flange vuqdwfyu jsat izkIr fd;k x;kA ls vuqikr j[kus ij when clearance angle (d) is plotted against n/v. Weeding fDy;jsal dks.k 5.25 fMxzh ls 5.84 fMxzh b"Vre izkIr gqvkA bl efficiency increased with the increase in the ratio of n/v vuqikr esa funkbZ dk izfr’kr 87-90% izkIr gqvkA which leads to increase in clearance angle (d) as per the equation and practice. v?;;u ds vuqlkj ;g irk pyrk gs fd b"Vre dk;Z gsrq n/v dks The optimization of clearance angle (d) for rotary 8:1 ls 12 ds chp pykuk leqfpr gksxk A tool was different with the length of tools on the flange with ratio of forward speed and rotation of the tool. But it is important to mention that the clearance angle (d) with References R=28 cm for n/v at lower ranges is negative or very low which causes difficulty in penetration of tool in the Kurchania SP, Rathi GS, Bhaua CS, Mathew R (2001) Bio- soil and subsequently weed elimination percentage is efficiency of post emergence herbicides for weed reduced. This causes excessive consumption of power control in soybean (Glycine max). Indian J Weed Sci 33: 34-7 and energy besides the tool walking out of the field. As the clearance angle (d) increased the tool Muniyappa TV, Prasad Ramachandra, Krishnamurthy K eliminated more weeds and also helped to slash the (1986) Critical weed competition in soybean. Indian weed plant and shook away the soil from the weed plant J Weed Sci 18: 34-8 root. The clearance angle (d) with the R=30 cm and z=3 Sakai J, Hai LV (1982) Design theories of single edged blade was found to be optimum as the ratio of n/v ranging from (Manuscript Received: 25.05.2011; Accepted: 19.10.2011) for Japanese Rotary Blades. JSAM 44(3):411-416
Tiwari JP, Kurchania SP (1990) Survey and management of soybean (Glycine max) ecosystem in Madhya Pradesh. Indian J Agric Sci 60: 672-6
191 JNKVV Res J 45(2): 192-198 (2011)
Agricultural resources mapping in GIS of Kymore Plateau and Satpura Hills agro-climatic zone, Madhya Pradesh
Varun Sharma and M.K. Hardaha* Industrial Development Bank of India, Barnala, Punjab 148101 *Directorate of Extension Services Jawaharlal Nehru Kirishi Vishwa Vidyalaya, Jabalpur 482004 (MP)
involved many different sources of printed information Abstract such as soil survey manuals, topographic maps, aerial photographs, vegetation surveys, flood maps, hydrology Agricultural production system needs characterizing its maps, and property surveys to name a few. Each source resources. Most of the agricultural resources are found varying contributed an important characteristic to the final with space and time. Geographical Information System (GIS) decision. Human decision-makers were challenged to can store, analyze and retrieve information that pertains to keep track of all this information at once, to understand various locations therefore an attempt have been made to characterize agricultural resources for Kymore Plateau and the interrelationships, and to correlate multiple data Satpura Hills agro – climatic zone of Madhya Pradesh. sources at single locations. Resources data were tabulated in MS Access software, which Advances have been made towards extraordinary digital was directly used by GIS software. Database generated includes attributes regarding area under different land use, systems for utilization in land-use planning. Computer grain crops, pulses, cash crops, oilseeds, medicinal crops, programs including decision support systems (models), animals, farm implements and machines, rainfall and irrigation Geographic Information Systems (GIS), spreadsheets, sources. Maps on various themes have been prepared which databases, and color desktop publishing programs can be useful in characterizing agricultural resources of the contribute to the speed and efficiency of the overall zone and developing decision support system. To demonstrate planning process. In recent years water and agricultural the utility of thematic maps quarries were developed for resources mapping on GIS platform is getting attention identification of blocks requiring improved practices of water of research and development personals (Kar 2000; Singh management in irrigated area by wells and identification of and Prakash 2001; Natarajan 2001; Kumar and Prasad blocks for promotion of land use activities like intensive cropping, horticulture and agro-forestry. It was found that 18 2001; Dubey 2002; Bhattacharyya 2003; Gupta and blocks were identified which needs attention for improving Gupta 2004). water management practices. Fifteen blocks were found where There is a need to have a diversified land use intensive cropping should be promoted. system for better utilization of natural resources in general and agricultural resources in particular. Keywords: Resource mapping, geographical information Therefore a study has been taken up with the specific system objective of mapping of agricultural resources of the Kymore Plateau and Satpura Hills for agricultural Land Cover is the physical state of the land surface with the combination of vegetation, soil, rock, water and human made structures. The term land use relates to the human activities associated with specific piece of land. Wise land-use planning involves making knowledgeable decisions about land use and the environment. Holistic planning involves input from multiple, interrelated data sources and types. In order to accomplish this feat a great deal of information must be Fig.1. Location of Agro-climatic Zone –IV of Madhya considered simultaneously. Traditional land-use planning Pradesh
192 resources using GIS technique and demonstrating its Digitized block boundary map was connected to external application. database (MS Access) having the common Block-ID in block boundary vector map and external database. To establish a relationship between the feature classes, Material and methods “join” was created. It facilitates to perform multi-field joins on feature classes and queries. The agro-climatic zone–IV of Madhya Pradesh is known as Kymore Plateau and Satpura Hills, comprises of Preparation of thematic maps Seoni, Jabalpur, Katni, Panna, Rewa, Satna and Sidhi districts (Fig.1). The total geographical area of the zone is 46.81 lakh ha, accounting for 11.3 per cent of the total A thematic display symbolized geographic feature area of the State. according to non-graphic attribute data through the use of colour and other defined display properties. A thematic Data required for preparation of relational display was created from feature class in any open database for agricultural resources were obtained from warehouse connection, or from query in the active Geo District Statistical Book of concerned districts. All data workspace and the attribute was classified as density/ used in the study are related to year 2003-04 for Katni, intensity, count or range representing useful information. Rewa, Seoni, Panna; 2004-05 for Sidhi and 2005-06 for Satna and Jabalpur. Unit for generation of database of agricultural resources is considered as development Application of thematic maps on GIS platform block. Spatial information of soils in the zone used for digitization is obtained from soil report (Tomar et al. 1995). The main advantage of GIS software is that it enables Resources data obtained were tabulated in MS mathematical and logical operations on spatial and Access. ID number was given to each block having 6 attribute data. To demonstrate the practical utility of digits. First four digits are the ID for the district as per thematic maps two quarries were build up. standards for GIS in India (NAARM 2005). Last two digits indicate the block number of the district, which were Identification of blocks requiring improved water assigned according to location of the block starting from management practices south and moving towards north. The specifications for map generation on GIS are- Training campaign for farmers is to be launched. Let · Projection system: POLYCONIC there be a query - what are the blocks where water · Coordinate system: Latitude-Longitude (Lat-Lon) application efficiency is poor for well irrigation? So that · Datum: India 1975 geodetic datum training and demonstration module can be planned in · Digitize reference point (TICS) browsed on Lat-Lon those blocks. To answer the question, a query in GIS (Table 1) platform was generated with thematic maps and database generated. Following assumptions were made Table 1. Longitude and Latitude of TICS to work out existing water application status in well Reference Point Longitude Latitude command.
Barghat 79:43:54.584 22:01:39.724 Annual draft of water for open well is 1.0-1.5 ha-m/ Singrauli 82:39:08.946 24:04:08.116 • Teonthar 81:38:21.491 24:58:56.266 year/well and for tubes well it is 2.0-3.0 ha-m/year/ Ajaygarh 80:15:20.268 24:54:24.052 well (CGWB, 2003). Average value of draft was considered as 1.25 ha-m/year/well for open well and 2.50 ha-m/year/well for tube well. The registered raster image (Kymore Plateau and In any block, out of the total number of wells Satpura Hills) was digitized by tracing over the • available, 70% of the wells are open wells and boundaries on the screen to turn into vector data for remaining 30% are tube wells/ bore wells. zone boundary, district boundary, block boundary, rain gauge station (point feature class) and town name (point • Water requirement for rabi crops is 40 cm. feature class). • Overall efficiency of irrigation from well is 65 percent. With above assumptions, on an average a well should Connecting external database irrigate 2.65 ha. A function attribute command area of
193 100 wells (WELLCOM) was calculated as geographical area. • Tractor Density: Tractor density is an indicator of WELLCOM = Well irrigated area x 100/ Number of wells. mechanization in agricultural sectors. On an average one tractor for 20 ha land is required for mechanized farming. In the zone-IV, no block full Blocks where well irrigated area by 100 wells is in the fills this requirement. Hence it was considered that range of 215 to 315 ha (average 265) is considered the blocks, which have one tractor for 50 ha, land standard or medium. A thematic prepared for attribute (tractor density more than 20) can be enhanced to WELLCOM with three classes (Table 2) was prepared. desired density of 50 by promoting activities for Blocks which are rated as Poor, require measures for agricultural mechanization and financial improved practices of water management. arrangements. • Soil Texture: Soil indicator makes it little easier to Table 2. Classification of well irrigated area indicate the area for promoting various agricultural Irrigated area (ha) Well irrigation status activities. For intensive agriculture medium black per 100 wells soil and light black soil is required. Soil texture is grouped in medium black soil (MBS), light black 0 – 215 Poor soil (LBS) and medium red and black soil (MRBS). 215 – 315 Medium 315 or more Good • Soil Depth: Soil depth can be used as an indicator of slope. To get the idea of land slope, soil depth is the criteria used which is grouped into deep, shallow Identification of blocks for promotion of crop and moderate. For intensive agriculture deep and diversification activities moderate soil depth are required.
There can be many requirements for developing action plan for crop diversification use. Looking to the database On the basis of these five criteria’s a query has been generated and simplicity in modeling following five criteria made and land use has been suggested (Table 4). were considered for developing land use plan. The criteria are based on recommendation of IMSD technical Table 4. Guidelines for promotion of land use activity guideline (IMSD 1995) with some modification. Irrigated Land use Tractor Soil Soil Promotion status use (LU) density texture depth of activity • Percentage irrigated area: The important (IRA) (ST) (SD) requirement for intensive agriculture is availability Good Good > 20 MBS Deep, Intensive of irrigation facility from all sources of irrigation. LBS Moderate Cropping Medium, Poor 20 LBS, Moderate Horticulture Blocks where ratio of irrigated area and net sown Good Medium MRBS Shallow area is less than 0.20 are considered as rain-fed Poor Poor, 20 LBS, Shallow Agro- areas (WARSA 1996). A thematic map has been prepared based on a functional attribute (IRA = Total irrigated area x 100/ net sown area) as described Table 3.
Table 3. Classification of irrigated area IRSclass Irrigation status Percent irrigated area 1 Poor <20 2 Medium 20-39 3 Good 40 or more Fig.2. Digital map of block boundaries
Medium MRBS forestry Present agricultural land use: The existing land use Rest of all Cropping with • other is always important in deciding action plan for land entrepreneurship utilization. In this report land use for agriculture has been classified on the basis of a new attribute (LU), calculated as percentage of net sown area to
194 Results and discussion of Soil in the zone varies from shallow to deep with maximum area of 27.21 lakh ha covered under deep soils (Fig.4). A scanned map in TIFF format has been imported in geo workspace for on screen registration and digitization. The digitized maps of zone, district boundary and block Thematic maps boundary have been prepared. Digital map in vector format of block boundary is depicted in Fig.2. Database for other agricultural attributes (Table 5) have been prepared and thematic map can be prepared Resource mapping instantaneously in the GIS platform. Some of the thematic maps prepared are presented. Soils Crops Three types of soil namely viz., medium black and red soil, medium black soil and light black soil are found in Rice is the major kharif crop of the zone. Block wise the study area. A maximum area of 27.23 lakh ha in 28 area under paddy crop is depicted in Fig.5. It can be development blocks, is covered by medium black soil seen that depicted from the map that 21 blocks have (Fig. 3). The light black soil (LBS) is distributed in 8 area in the range 3500 to 11375 ha , 22 blocks have the blocks; where as mixed red and black soil (MRBS) is range 11376 to 19250 ha, 4 blocks each in the range distributed in 15 blocks. Medium black soils are mostly 19251 to 27125 ha and 27126 to 35000 ha. A map of found in Panna, Satna, Rewa and Sidhi districts. Depth area under wheat crops in various blocks of the zone
Fig. 3 Soils in Kymore Plateau & Satpura Hills Fig. 5. Area under paddy crop in blocks of Kymore Plateau & Satpura Hills
Fig. 4. Variation in soil depth in Kymore Plateau & Fig. 6. Area under wheat crop in blocks of Kymore Satpura Hills Plateau & Satpura Hills
195 Table 5. Attributes of resources database prepared for Kymore Plateau & Satpura Hills agroclimatic zone – 4
Database Attribute Grain Wheat, Sorghum, Maize, Other grain crops, Total grain crops Pulses Chickpea, Pigeon pea, Black gram, Green gram, Lentil, Other pulses, Total pulses Cash crops Sugarcane, Total fruits, Total vegetables, Total spices, Cotton, Other fiber, Total fiber Oil seeds Sesame, Linseed, Groundnut, Mustard, Soybean, Other oilseeds, Total oilseeds Season wise Crops Kharif food crops, Kharif non food crops, Rabi food crops, Kharif non food crops, Total of Kharif and rabi crops Irrigation Number of canals, Canal irrigated area, Number of hand pump, Hand pump irrigated area, Number of wells, Well irrigated area, Number of ponds, Pond irrigated area ,Other source irrigated area, Total source irrigated area, More than one irrigated area, Total irrigated area, Percent of pure irrigated area to pure sown area Land use Geographical area, Forest area, Land not available for agriculture, Non agricultural land, Agricultural land, Fallow land, Pure sown area, Double crop area, Total of pure sown area and double crop Medicinal Tobacco, Other medicinal and aromatic plants, Total medicinal and aromatic, plants, Chari, Various non food crops, Total non food crops, Total of food and non-food crops Animal and Bird Number of bullocks, Number of cows, Calf cows, Total cow, Number of He- buffalo, She buffalo, Calf buffalo, Total buffalo, Sheep, Goat, Horse, Ass, Donkey, Camel, Pig, Total animals, Total hen, Duck, Total Poultry Machinery Wood plough, Iron plough, Bullock cart, Oil for pump, Electricity for pump, Number of tractors, Dhaani, Power operated cane crusher, Animal operated cane crusher Rainfall Year wise rainfall in mm
has been prepared (Fig.6). It can be visualized form the map that 16 blocks have area under wheat in the range 2500 to 9375 ha, 24 blocks have the area under wheat in the range 9376 to 16250 ha, 8 blocks have the range of 16251 to 23125 ha, and 3 blocks have area under wheat in the range of 23126 to 30000 ha.
Animals
Thematic maps on animals were prepared including total number of cow, total buffalo, poultry birds, total animal etc. From the thematic map of total animals it can be observed that maximum number of animals is in Panna Fig. 7. Number of animals in blocks of Kymore block of Panna district (Fig. 7). A map prepared for total Plateau & Satpura Hills number of poultry birds (Fig. 8) it is recoded that 39 blocks have number of poultry in the range 2000 to 201500, two blocks have in the range 600501 to 800000, two blocks have in the range 201501 to 401000. The maximum number of poultry birds in the zone is in Jabalpur block of Jabalpur district.
Irrigation
A map on number of wells used for irrigation has been prepared (Fig. 9). There are 32 blocks having number of wells less than 1700, fifteen blocks having number of wells in the range 1701 to 3400, three blocks having number of wells in the range 3401 to 5100, one block Fig. 8. Number of poultry bird in blocks of Kymore (Seoni) is having highest number of wells. Number of Plateau & Satpura Hills
196 Wells used for irrigation in a block is an indicator of the water table of the area, geological formation.
Agricultural machinery
The maximum numbers of tractors (more than 2000) are in Nagod block of Satna district (Fig. 10) with tractor density of 40, whereas minimum numbers of tractors are in Shahanagar block of Panna districts. This indicates the poor mechanization in Shahnagar block. There are 39 blocks having number of tractors less than 700, ten blocks having tractors in the range 701 to 1400 and one Fig.11. Well irrigation status in blocks of Kymore each in the range 1401 to 2100 and 2101 to 2800, Plateau & Satpura Hills respectively. The mode of working in the field in these blocks is iron & wooden plough.
Application of GIS
Identification of blocks requiring improved practices of water management
The result obtained from the query for the identification of well irrigated area is generated in a thematic map. In 18 blocks well status is poor shows that training and demonstration can be organized for improved water Fig.12. Suitability map for the promotion of land use activity
management (Fig.11).
Identification of blocks for promotion of land use activity
The result obtained from the query for suitable land use activities in blocks is generated in thematic map (Fig.12). From the thematic map of promotion of land use activity it can be depicted that intensive agriculture can be Fig. 9. Number of wells in blocks of Kymore Plateau & promoted 15 blocks, horticulture in 28 blocks, agro- Satpura Hills forestry in 4 blocks and agriculture with other entrepreneurship in 4 blocks. Geographical information system is an important tool for mapping resources and characterization of a region. Spatial and attribute data clubbed together in GIS platform makes development of models and decision support system faster and easier.
—f"k mRiknu iz.kkyh dh t:jr gS vius lalk/kuksa ds y{k.k o.kZu dh A —f"k lalk/ku vf/kdka’kr% LFkku vkSj le; ds lkFk cnyrs gq, ik, Fig.10. Number of tractors in blocks of Kymore tkrs gSA HkkSxksfyd lwpuk iz.kkyh ¼th-vkbZ-,l½ fofHkUu LFkkuksa ls Plateau & Satpura Hills
197 lacaf/kr tkudkjh dk Hk.Mkj.k] fo’ys"k.k ,oa iqu% izn’kZu dj ldrh Engineering JNKVV Jabalpur Gupta RD, Gupta S (2004) Water resources development gS A blfy, e/;izns’k ds dSeksj iBkj ,oa lriqMk fgYl —f"k tyok;q action plan using remote sensing data-a case study {ks= ds —f"k lalk/kuksa ds ekufp=hdj.k dk ,d iz;kl fd;k x;k gSA in Dhaulpur block, Dhaulpur district, Rajasthan. lalk/ku lacaf/kr vkdMksa dks ,e-,l- ,Dlsl lk¶Vos;j esa URL:
198 JNKVV Res J 45(2): 199-202 (2011)
Seed coverer- a tool to enhance field emergence and productivity of soybean
Devvrat Singh, A.K. Vyas, R. Ramteke, S.D. Billore and I.R. Khan Directorate of Soybean Research Indian Council of Agricultural Research Indore 452001 (MP)
Abstract Gummerson 1986, Helms et al. 1996, Ferriss et al. 1987, An effective device to cover soybean seed after seeding with Wheeler and Ellis 1992, Hamman et al. 2002). seed drill was developed at Directorate of Soybean Research, Considering the above fact, uniform placing of seeds in Indore. Attaching the developed device on shanks of seed the soil and properly covering of soil is necessary for drill effectively improves field emergence of soybean cv ‘JS epigeal germination and better plant population of 335’ as judged by the increase in resultant plant population soybean. Therefore, attention was drawn to overcome to the extent of 42 %. As compared to traditional seed covering this problem. Poor plant population is considered to be by attaching angle iron bar behind seed drill also, the field one of the constraints in obtaining optimum yield. Hence, emergence was 19 % higher than non-use of seed coverer. an attempt has been made to develop an effective seed The increased field emergence resulted in a yield increase of covering mechanism, which turns out to be capable of 28 and 24 % with the use of seed coverer and angle iron bar, respectively over control. instant covering of seed in planted rows with sowing operation with seed drills. The seed-covering device developed at Directorate of Soybean Research was Keywords: Seed coverer, seed drills and plant population evaluated for consecutive three years and the results obtained are presented in this paper.
S o y b e a n [ Glycine max (L.) Merrill] cultivation has established well in the Central India and other upcoming Material and methods regions during past three and half decades. The area and productivity of crop in initial stages of commercial exploitation in 1970 was merely 30,000 ha and 426 kg/ Development of seed coverer ha which is now more than 7 million ha with productivity of about 1000 kg/ha. Looking to the potential of newly A simple seed coverer device was developed at developed varieties (about 3500 kg/ha) and realizable Directorate of Soybean Research to facilitate covering potential at farmers field as 1846 kg/ha (Billore et al. of row planted seed using a seed drill. The developed 2009), it appears feasible to enhance the yield levels in device (Plate 1) is made of mild steel with dimensions of the country. Of late, there has been emphasis on the 16 cm (width) and 22.5 cm (height) and is attachable “Precision Agriculture”, which envisages lying attention with shank of the seed drill using metallic fitment. The on the finer management of crop so that cumulative effect total weight with the fitment works out to 1.4 kg. In on productivity enhancement can be achieved.Soybean operation, the inverted V notch with base of 10 cm with seed being a ‘poor storer’ loses viability very rapidly under height of 8 cm pushes the soil covering the seed warm and humid conditions of storage, hence, the major completely. Weight of the Seed covering device was of constraints in soybean cultivation is the non-availability 2.5 Kg/unit which include support system which is of high vigour seed at the time of sowing (Singh and attached to the tine of the seed drill. Any quality Dadlani 2003; Babu and Hunje 2008) for better field manufacturer with a mere cost of Rs 180 per piece can emergence. Further, it is influenced by the quality of the fabricate this simple device with fitment. seed planted and by the seedbed environment, with soil moisture, pathogens, temperature and impedance being The afore mentioned developed seed coverer the most important components (Ramteke et al. 2009, w a s e va lu a te d fo r 3 ye a rs (2 0 0 7 to 2 0 0 9 ) d u rin g kharif
199 season for soybean crop and the performance was situated at 22º 4’37’’N latitude, 75º 52’7’’E longitude and compared with traditional seed covering (running an altitude of 540 meter above the mean sea level. angle iron bar behind the tractor) and without the use of above two. It was ensured that the seed depth in all the three treatments was uniform as recommended in Results and discussion package of practices (Bhatnagar and Tiwari 1995). For the purpose, the planting of soybean was carried out in Effect on plant population 1.8 m x 50 m plots utilizing all the three treatments randomly in 10 replicates. The plant population was Highly significant differences in plant population have recorded after 15 days of planting in one square meter been noted among the three treatments during each year area at 10 places in a treatment and average population and in the pooled analysis (Table 1). Planting soybean was worked out. The soybean variety (JS 335) was without any arrangement for covering of seed in rows planted between 15th and 20th July during the three with soil led to the lowest plant population (Fig 1). On years. As per recommended practice, on account of late the pooled basis, the improvement in plant population sowing, the seed rate was enhanced by 25 % and row- by attaching an angle iron rod behind seed drill improved to-row distance was kept at 30 cm. The crop was the population by 19 %the use of developed covering harvested at harvest maturity and seed yield was device resulted in increased plant population of 42 % reported as kg/ha. over treatment without seed covering. This indicates that The data were analyzed following Singh and resorting to seed covering either by angle iron rod and Chaudhary (1985).The experiments were conducted on more effectively by NRC for Soybean developed seed deep black cotton soils with pH 7.6 to 8.1 (Typic coverer, not only improves germination but also Chromusterts and Lithic Vertic Ustochrepts) in a subsequent emergence of seed. It also suggests that Randomized Block Design at experimental farm of the this finding may be of more of practical utility for bold Directorate of Soybean Research, Indore (MP) that is seeded varieties like JS 71 05, Ahilya 3, PK 472 etc, Table 1. Utility of seed coverer in improving the performance of soybean (JS 335)
Plant population (plants/m2) Seed yield (kg/ha) Treatments 2007 2008 2009 Mean % over 2007 2008 2009 Mean % over control control With seed coverer 24.2 25.3 24.4 24.6 42.19 1142 1123 1334 1166 28.1 Seed covering with angle 19.9 20.8 21.0 20.6 10.40 1052 1057 1268 1126 23.7 iron bar Without seed coverer 16.6 17.7 17.6 17.3 0.00 841 851 1040 910 0.00 CD (p = 0.05) 1.04 0.65 0.94 0.493 25.54 8.29 18.46 10.36 CD (p = 0.01) 1.42 0.88 1.29 0.656 35.0 11.35 25.3 13.81
Comparison of plant population Comparison of yields With seed coverer Seed covering with angle iron bar Without seed coverer With seed coverer Seed covering with angle iron bar Without seed coverer
30 1600
25 1400 1200 20 1000 15 800 Yield/msq 10
population/ msq population/ 600 400 5 200 0 0 2007 2008 2009 2007 2008 2009 Year Year
Fig 1 Effect of seed coverer on plant population and seed yield of soybean
200 which are shy in germination and field emergence. The has been noticed as the performance of rain fed soybean use of this device will curb the practice among the is directly related to the quantity and distribution of rains. farmers to use 1.5 to two times the recommended seed The study suggests that attaching the seed rate on account of fear of low germinability and field coverer device along with seed drill for planting soybean emergence of seed ultimately reducing the cost of developed by Directorate of Soybean Research Indore cultivation. Seed and sowing is the costly input in facilitates better emergence and field establishment cultivation of soybean after intercultural operations and enhancing the seed yield. This also helps farmers to weed management (Jain 1986). use recommended seed rate thereby avoiding the problems associated with over and under seeding Effect on seed yield economizing the cost of cultivation.
The data recorded in seed yield of soybean also revealed References that like plant population, the productivity of soybean is also significantly influenced by the treatments during all Babu H M M , Hunje R (2008) Effect of seed treatment with the years of experimentation as well as in pooled analysis botanicals on storability of soybean. Karnataka J (Table 1). The increase in seed yield by merely running Agric Sci 21(3): 357-360 an angle iron bar behind the seed drill to cover the seeds Bhatnagar P S ,Tiwari S P (1995) Technology for Increasing in rows revealed an increase of 22 to 24 % during the Soybean Production in India. NRCS Technical three years of experimentation with an average increase Bulletin 1 (Revised) p 49 of 24 %. Use of seed coverer over its non-use resulted Billore S D, Vyas A K , Joshi OP (2009) Assessment of in 28 % increase in soybean seed yield (Fig 1). The yield improved production technologies of soybean on increase with optimum plant population (Bhatnagar and production and economic potentials in India. J Food Tiwari 1995) appears to be logical. The study (Zhou et Leg 22(I): 49-52 al. 2010) in China also indicated that enhanced Ferriss R S, Stuckey R E, Gleason M L, Siegel M R (1987) productivity and water use efficiency (WUE) of rainfed Effect of seed quality, seed treatment, soil source, summer soybean could be achieved via row spacing and initial soil moisture on soybean seedling reduction and plant spacing widening under uniform performance. Phytopathology 77: 140–148 planting density. The year-to-year variation in the yield Gummerson R S (1986) The effect of constant temperatures
201 and osmotic potentials on the germination of sugar- Evaluation of soybean [Glycine max (L.) Merrill] beet. J Expetl Bot 37: 729–741 genotypes for germination, field emergence and Hamman B, Egli D B , Koning G (2002) Seed vigor, soilborne yield. J Oilseeds Res 26 (Spl. issue): 81-83 pathogens, preemergent growth, and soybean Singh K K , Dadlani M (2003) Effect of packaging on vigour seedling emergence. Crop Sci 42: 451–457 and viability of soybean [Glycine max (L.) Merrill] Helms TC, Deckard E L, Goos R J, Enz J W (1996) Soil seed during ambient storage. Seed Res 31 (1) 27- moisture, temperature, and drying influence on 32 soybean emergence. Agro J 88: 662–667 Singh R K , Chaudhary B D (1985) Biometrical methods in Jain B L (1986) Constraint analysis of soybean in Sehore quantitative genetic analysis. Kalyani Publ (Third Ed (MP) Project Coordinator’s report and summary 1985) pp. 318 tables of experiments 1985-86 AICRPS p 321 Wheeler T R , Ellis R H (1992) Seed quality and seedling Ramteke R, Gupta G K, Singh V L , Rathore R K S (2009) (Manuscriptemergence Received: 25.05.2011;in onion (Allium Accepted: cepa 27.07.2011)L.). J Horti Sci 67: 319–332 Zhou X B, Yang G M, Sun S J ,Chen Y H (2010) Plant and row spacing effects on soil water and yield of rainfed summer soybean in the northern China. Plant Soil Envin 56(1): 1–7
202 JNKVV Res J 45(2): 203-204 (2011)
Design parameters of border irrigation for clay loam soil
K.L. Mishra and M.K. Awasthi Department of Soil and Water Engineering College of Agricultural Engineering Jawaharlal Nehru Krishi Vishwa Vidyalaya Jabalpur 482004 (MP)
Abstract Experiment was conducted for various design parameters of border lengths 1x22m and 1.5 x 22m. In surface irrigation the border strip method is the most Size of stream was taken as 1.0 liters per second practiced one. Proper designing of borders is essential to get per meter width of the border. A plot of rate of advance its maximum advantage. In present paper, the hydraulic resistance, manning’s roughness coefficient and application and recession is presented in Fig 1. The recession efficiency are reported for the clay loam soil of main campus started just after cutting the stream at 20 minutes and of JNKVV. prolonged for 93 minutes. However, during meantime the advance was continuing till it reaches to the tail at 32 minutes. The difference between the water front reaches Keywords: Border irrigation, hydraulic resistance, a particular point along the border and the time at which application efficiency the tail water recesses from the same point is the infiltration opportunity time (IOT). The opportunity time The purpose of irrigations is to provide moisture, which varies from 21 minutes at the head to 43 minutes at the is essential for plant growth. Border irrigation is one of tail end. The IOT may be used in deciding depth of the methods of surface irrigation. Border method of irrigation to this soil. The infiltration characteristics of the irrigation makes the use of parallel ridges to guide a sheet soil were determined by conducting concentric ring of flowing water as it moves down the slope. The irrigation infiltrometer test. In Fig 2 the infiltration characteristics stream must be large enough to spread over the entire of the soil are given. Figure indicates the two curves i.e. width between the border ridges and should be turned off when the advancing water front either reaches the Fig. 1 Advance and recession of water front lower end or a few minutes before or after that. The water 100 temporarily stored in the border moves down the strip 80 and infiltrates, thus completing the irrigation. 60 Variables which are involved in the hydraulics and design 40
20
of surface irrigation system are:- Elapsed time, min
0 Size of stream, rate of Advance of water over the 0 5 10 15 20 25 30 land surface, surface roughness, length of run and time Distance from border head, m required, depth of flow over the land surface, slope of the land surface, rate of movement of water into the soil Fig 2 Infiltration characteristics of study area
(i.e. intake rate), depth of water applied. 45 40 35 30 Materials and methods 25 20 15 The present study for estimation of the design 10 and Infitra tion ra te , c5 m /h C um m ulative infiltrs tion, cm parameters per strip method was under taken. Different 0 0 20 40 60 80 variables were evolved for clay loam of JNKVV Jabalpur. Elapsed time, min
203 average infiltration rate and accumulated infiltration depth Five soil sampling stations were at 0, 5, 11,16.5 and 22 as a function of time. The characteristics equation is m from the border head. Samples were collected at four y = 0.59 t0.49 +0.28 incremental depths of 15 cm. the moisture constants were determined by oven dry method. After computing Where, y is accumulated depth of infiltration in cm per available water perm depth the application efficiency was hour with respect to elapsed time t in minutes. calculated using following relationship. Application efficiency= Water stored in the root zone / Water Results and Discussion applied to the field The application efficiency were computed as 60.35% Hydraulic Resistance for 1 m x 22 m border and 55.25% for 1.5 m x 22 m
The Darcy - Weisbach resistance co-efficient for the non- Conclusion vegetated border strips was calculated from the modified Blasius formula(1908) applicable to border strips which The derived parameters viz. Darcy - weishbach is expressed as follows: resistance co-efficient found out as 0.032 and 0.422, f= (0.316)/4q/v)1/4 the Manning’s roughness co-efficient was found out to be 0.0155 and 0.0145 and the application efficiency were Where, computed as 60.35% and 55.25% for 1 m x 22 m border f= Darcy-Weisbacn resistance co-efficient and for 1.5 m x 22 m respectively. These parameters are useful in border irrigation design for clay loam soil of q= Discharge (liter/sec.) JNKVV farm specifically soils behind College of 2 v= Kinematic viscosity of water (cm /sec.) Agricultural Engineering. Jabalpur
The Darcy - weishbach resistance co- lrgh flapkbZ fof/k;ksa esa ckMZj i)fr vxz.kh gSA ckMZj i)fr ls efficient(1857) found out as 0.032 for size of 1 m x22 m and 0.422 for border size 1.5 m x 22 m. leqfpr ykHk ysus ds fy;s bldh mfpr fMtkbu gksuk vko’;d gSA izLrqr 'kks/k i= esa tokgjyky usg: —f"k fo’ofo|ky; ds Manning’s Roughness Co-efficient- eq[;ky; esa ikbZ tkus okys Dys ykse e`nk ds fy;s ckMZj fof/k ds izeq[k rRo ;Fkk gkbMªkfyd vojks/k] esafuaXl ?k"kZ.k xq.kkad ,oa flapkbZ n{krk In non-vegetated borders, when the hydraulic resistance dk vkdyu fd;k x;k gSA is expressed by the Darcy-Weisbach friction factor f, the equivalent value of manning n is calculated using the References following relationship: = (R2/3 f ) 8g Blasius, H. (1908), Grenzschichten in Flüssigkeiten mit kleiner in which Reibung, Z. Math. Phys. vol 56, pp. 1-37. http:// naca.central.cranfield.ac.uk/reports/1950/naca-tm- R=hydraulic radius in meters 1256.pdf (English translation) g=Acceleration due to gravity in m/sec2 f= Darcy-Weisbach friction factor Darcy, H. (1857). Recherches Experimentales Relatives au The Manning’s roughness co-efficient was found Mouvement de L’Eau dans les Tuyaux(“Experimental Research Relating to the Movement of Water in out to be 0.0155 for 1mx22mts border size and 0.0145 Pipes). Mallet-Bachelier, Paris. 268 p. & atlas. for 1.5 x 22 m border. Mohan AM, Swaminathan KR(1972) Design and evaluation Application Efficiency of irrigation method, Water Technology Center, IARI, New Delhi
The application efficiency was determined by conducting Tyagi NK Lal R(1970) Predicting water advance in furrows. the experiments in the field. Soil moisture samples were J. Agril.Engg.(4): 132-143. taken at distances from the head of the border just before the water application and 48 hour after the application. (Manuscript Received : 25.05.2011; Accepted 15.08.2011)
204 JNKVV Res J 45(2): 205-209 (2011)
Planning and design of drainage system in Gopagwari village in the foot hills of Bargi dam
K.L. Mishra and M.K. Awasthi Department of Soil and Water Engineering College of Agricultural Engineering Jawaharlal Nehru Krishi Vishwa Vidyalaya Jabalpur 482004 (MP)
Abstract Material and methods
In the present work design aspects of sub-surface drainage Description of the area is considered for planning in Gopagwari village of Bargi block of Jabalpur district. A reconnaissance survey for the assessment of the ground water and drain ability of the The study area is situated on the foothills of the Bargi proposed area is done to plan the system. The study area is dam near the Right Bank Canal (RBC), which is about situated on the foothills of the Bargi dam near the Right Bank 43 km from Jabalpur. The R.B.C. takes off from the right Canal (R.B.C.) To find the extent of water logging in the area, flank of Bargi dam. The area lies in between the latitude observation wells were laid out. Almost an area of 50 ha is 22°52’30” and 23°26’30” and longitude 78°45’E and affected by water logging in the vicinity of the dam in R.B.C. 79°54’E covers 950x650m. The major crop of this region command. There are 36 laterals are proposed of length 624 is wheat. The area has considerable variations in rainfall, m in West to East direction and inside diameter of each is 10 temperature and humidity. The rainy season extends cm the mains of the length 924 m from north to south and from June to October under the influence of the south diameter of 30 cm is proposed. The total design discharge west monsoon. It also receives rainfall during the month from 36 lateral is 0.9109 cumec and from the main it is 0.918 of January. The average annual rainfall is about cumec. Two motors of 40 HP were suited for the purpose. 1345mm. The temperature during the summer season ranges from about 35 to 45°C and during the winters it is between 30°C and 5°C. The maximum relative Keywords: Drainage design, Pore volume percentage, humidity ranges between 17 to 90% in August. Isobar- isobath
Topography Drainage is a tool to manage ground water level, plays an important role in maintaining and improving crop yields. Sub-surface drainage is the removal or control of The elevation above the mean sea level varies from 403.03 to 407.43 m. The topography of the area is ground water and the removal or control of salts, using undulating and rolling due to hilly area. The surface water as the vehicle( Agrawal and Malik 1985). The texture of the area is clay loam. Permeability is slow to source of water may be percolation from precipitation or moderate in upper layers while moderate to rapid in the irrigation; leakage from canals, drains or surface water lower layers. The pH was calculated to be about 6.9.To bodies at higher elevation or from artesian aquifer. In design the subsurface drainage system the following the present work design aspects of sub-surface drainage method was adopted: is considered. The system aims at controlling the water table that can be achieved by tube well drainage, open Demarcation of the area of water logging above 1.8m drains or subsurface drains (pipe drains or mole drains) A reconnaissance survey for the assessment of the ground water and drain ability of the proposed area is A series of observation wells were laid out in the region. done to plan the system. The P.V.C. pipe of 3m length and diameter of 0.1m were laid out. The pipe was perforate to 1.82 m from the
205 bottom. The perforated region is wound over by Nercule Pore volume (22%) of soil at 91.4 cm capillary coir to avoid choking of the perforations which were about pressure was recorded (Table 1). According to the 12mm in India. Regular readings from these observation climatic conditions and the requirement of cropping wells were taken into account. With the help of pattern the depths of the lines have been assumed as topographical survey of the area a contour map was 2.7m for keeping the water table at 1.8m below the land prepared using linear interpolation. The readings of the surface. The sub-surface drainage system is designed groundwater levels that were taken from the observation to lower down the water table from 0 m to 1.8m from the well were then plotted graphically. The isobar map was land surface within 3 days. drawn with the elevation of water table and the isobath map was drawn with depth to water table. Drainage co-efficient = Rate of Removal of water x drainable pore volume- Design of subsurface drainage system x 0.22 = 0.132m/day Keeping in view the characteristics of the area it was decided to plan out a sub-surface drainage system as Determination of spacing of lateral tile lines the soil is very fertile and surface drainage will cause a loss of cultivable land as well as the movement of the implements on the field will be hampered. For design q/k = ratio = 0.132/3 = 0.437, and m = 0.9m purpose (Luthin 1966)the tile drainage system was considered. It was accomplished by means of a series Spacing is evaluated from the graphical solution of of tiles laid in a continuous line at a specified depth and modified ellipse equation, (drainage of agricultural land, grade. So that the free water entering the system will U.S.D.A., 1972) flow out by gravity. A tile drainage system consists of a S = 26 m drainage outlet, tile main, sub mains and laterals. The laterals remove the free water from the soil and the sub mains and main carry the water to the drainage outlet. Pattern for laying laterals
Results and Discussion Length of field = 950m Spacing = 26 m To find the extent of water logging in the area, observation No. of laterals = 950/26 = 36.53 36 wells were laid out. In this region there is not much Thirty six laterals tile lines having a length of 624 m along fluctuation in the water level but depth to water table is with a main tile line of 950 m length were laid below the very low. Almost an area of 50 ha is affected by water ground surface. logging in the vicinity of the dam in R.B.C. command.
From the observations taken the isobar, isobaths Determination of diameter of lateral tile line and main and the topographic maps are drawn, (pre-monsoon)Fig tile line 1 and (post-monsoon) Fig 2. The maps indicate the route or the movement of the groundwater and the source of Determination of diameter of lateral tile line augmentation in the ground water. By proper lining of Catchments area of one lateral tile line the canal some of the seepage can be controlled but it is not possible to prevent water logging in the area. Hence = 624x26 = 16224m² = 1.6224 ha the design of subsurface drainage system was required. Diameter of one lateral for full flow Using empirical formula Table 1. Values of drainable pore volume at different d = 0.892 (dcxA)0.375 / S0.1875 capillary pressures d =Dia of tile for full flow (inch), dc=drainage co-efficient Suction head (cm) Pore-volume percentage (inch/day), A=Area in acres, S =Slope
91.4 22.0 d = 0.892 (0.132x4.02351)0.375 / (0.001)0.1875 60.0 17.4 44.1 11.3 = 2.56 inches inches 34.2 5.2 Diameter for ¾ flow
206 Cross section area of lateral = (4/3)2 7.068 inch² Determination of sump depth and diameter 4/3 cross section area of lateral = 9.42 inch² Dia for ¾ th flow = (9.42 x 4) / The depth of main lateral = 6m = 3.46 inch Taking the depth of sump = 4.5 m Design dia. of lateral 4 inch In which 1/3m is kept for silt deposition. Discharge for 2 minutes = 918.6 x 2 x 60 = 110232 lt Determination of diameter of main tile line = 110.232 m³ for two minutes Diameter of sump = (110.232 x 4) / 4.5 x = 4.6 5 Catchment area of main tile line m Diameter of sump chosen = 5m = 650 x 950 = 617500 m² = 61.75.ha = Diameter for full flow Design of pump d = 0.892 (0.132x 153.14)0.375 / (0.001)0.1875 = 10 inch Let us consider that pump is placed 0.5m higher than ground surface. Cross section area of main tile line = /4 (10)² = 79.44 inch² = 198.6cm² Total water head = 4 + 0.5 = 4.5m Cross section area of main tile line for ¾ th flow Water horse power = W.H.Q./75 = 4/3 x 79.44 = 105.92 inch² = 264.8 cm² W = wt. of 1 cubic metre of water Design diameter of main lateral H = head of water in metre = (105.92 x 4) / = 11.6 inch Q = discharge of pump in m³/ sec. 30 cm. W.H.P. = 1000 x 4.5 x 0.9185 / 75 = 55.06 Efficiency of pump = 70 percent Determination of the discharge of tile lines Actual horse power of pump = (55.06 x 1000)/70 = 78.68 80hp Choose the water pump = 80 H.P. Discharge for one lateral Thus 2 pumps of 40 H.P. is best suited for the selected Formula used water logged area Qr = A X q / 86400 Qr = relief drain discharge in m³/sec. (cumec.) Selection of envelop material q = drainage co-efficient (m/day) A = catchment area of one lateral in m2 The base material of the soil has been taken at the depth; Qr = (26 x 637 )0.132 / 86400 = 0.025 cumec. the drain is to be laid. Sample has been taken and = 25 lps mechanical analysis was made (Gupta 1986). The graph Total discharge for 36 lateral = 25 x 36 = 910.9 lps between the size of the particles and percent of smaller particles that size has been plotted. Two filter materials Qr = A X q / 86400 have been determined. It was observed that the filter = (13 x 937 ) 132 / 86400 = 0.01860 material should be filter 1 or filter 2 was the best suited. = 18.6 lps The first and second filters are selected according to design criteria based on the research by U.S. Bureau of reclamation and the corps of Engineers U.S. Army. The Total discharge for main tile line ordinary bedding condition has been selected for laying out the tile lines over the filter material. Following = 910.9 + 18.6 = 918.6 lps = 0.918 cumec. conditions were taken into consideration for this purpose. Design of outlet (outlet design consists of sump design, motor design, motor and centrifugal pump design) a. The gradation curve of the filter material should be generally parallel to that of the base martial.
207 208 Table 2. Mechanical analysis for filters Sieve No. Sieve size Wt. retained Accu. Retained Accu. Percent Percent (mm) (gms) wt. (gms) retained finer than 1 4.76 247.5 247.5 5.39 94.61 2 2.83 264.5 512.0 11.16 88.84 3 1.41 524.5 1036.5 22.6 77.4 4 1.0 442.5 1479 32.2 67.8 5 0.707 305.9 1784.9 38.9 61.1 6 0.595 382.8 2167.7 47.24 52.76 7 0.063 297.5 2465.2 53.7 46.3 8 0.015 1587.2 4052.4 88.32 11.68 9 0.009 281.8 4334.2 94.46 5.54 b. 50 percent size of filter / 50 percent size of base = Tkcyiqj ftys ds cjxh Cykd esa xksikXokjh xkao dh tyyXurk dk 12 to 58 fujkdj.k {ks= ds fy;s v/kks lrgh ty fudkl lajpuk dh fMtkbu c. 15 percent size of filter / 15 percent size of base 43 =12 to 40 dh xbZ lacaf/kr {ks= tcyiqj ls fdyksehVj nwj cjxh Mse ds 50 d. 15 percent size of filter / 85 percent size of base = Bhd cktw es nk;kh rV ugj ds cktw esa fLFkr gS] ftldk {kas=Qy less than 5 gS- gSA ty yXurk ds fujkdj.k gsrq dqy 36 ysVjy ykbu ftles ls izR;sd 624 eh- yackbZ dh fMtkbu fd;k x;k 1 daØhV dh ysVjy Observations have been taken for mechanical analysis ftldh O;kl 30 laseh gS] ds }kjk 0-9109 D;wlsd dk ty izokg and presented in Table 2. fudkyk tk ldrk gS] bl gsrq 40 ,pIkh dh nks eksVj izLrkfor dh xbZA To find the filter size following calculations were done: 1. 50% size of filter = 12 x 50% of base = 12 x 0.48 = 5.76 mm References 2. 15% size of filter = 12 x 15% of base Agrawal MC, Malik RK (1985) Study of water table depth = 12 x 0.058 = 1.02 mm behaviour in Haryana. Agricultural Engineering Today 3. 50% of size filter = 58 x 50% of base (9):52-57 = 58 x 0.48 = 27.84 mm Gupta SK(1986) Evaluation variability of drainage pore space 4. 15% size of filter = 40 x 15% of base in sub surface drainage experiment. J Agril Engg 3 = 40 x 0.085 = 3.4 mm (1): 74-79 5. 15% of tiller < 5 x 85% of base Luthin JN (1966) Drainage Engineering. Wiley Eastern Pvt < 5 x 2.3 Ltd 123-139
Conclusion (Manuscript Received: 25.05.2011; Accepted: 30.08.2011)
There are 36 laterals are proposed of length 624 m in West to East direction and inside diameter of each is 10 cm the mains of the length 924 m from north to south and diameter of 30 cm is proposed. The total design discharge from 36 lateral is 0.9109 cumec and from the main it is 0.918 cumec. Two motors of 40 HP were suited for the purpose.
209 JNKVV Res J 45(2): 210-218 (2011)
Development of artificial neural network based model for prediction of stream flow in Wainganga catchment, Balaghat (Madhya Pradesh)
K.K. Madankar and M.K. Hardaha* Gramin Vikas Trust, Raipur 492001 (Chhattisgarh) *Directorate of Extension Services Jawaharlal Nehru Kishi Vishwa Vidyalaya Jabalpur 482004 (MP)
Abstract neural network, is a mathematical model or computational model that tries to simulate the structure and/or functional aspects of biological neural networks. Runoff prediction is important in fields of engineering and It consists of an interconnected group of artificial neurons resource management. Wainganga catchment at Kumhari and processes information using a connectionist gauging station near Balaghat has been chosen for developing multiple linear regression analysis model and artificial neural approach to computation. Many reserchers (Sudheer network model. Daily stream flow at Kumhari and daily rainfall and Jain 2003; Cigizoglu 2003; Kisi 2004; Sarangi et of Balaghat, Seoni and Keolari for nineteen years was used al. 2005; Cigizoglu and Kisi 2005; Deka and for developing the models. It was found that multiple linear Chandramouli 2005; Kisi 2008) developed ANN based regression model with thirteen input variables performed runoff model and found that ANN based model better. An ANN model with 14 input variables with 7 neurons, performed better than classical models. single hidden layer and one output and learned with Levenberg-Marquardt (LM) algorithm performed best. Present investigation was conducted at Kumhari (Balaghat) gauging station on Wainganga river. To establish the true merit of ANNs relative to simpler Keywords: Artificial neural network, multiple linear statistical techniques, comparisons are also made regression, rainfall-runoff modeling, Levenberg- between the forecasting skill of the two ANNs and a Marquardt algorithm stepwise multiple linear regression models. A study has been carried out to analyze runoff at Kumhari station of The runoff is critical to many water resources activities, Wainganga River and develop model for its estimation, for example, design of flood protection works, protection with the specific objectives of developing a multiple linear of agricultural lands, planning of water storage and regression model and an Artificial Neural Network model release, etc. Rainfall-runoff is the most complex for prediction of stream flow. hydrological phenomenon to comprehend due to tremendous spatial variability of watershed characteristics and precipitation patterns, making the Material and methods physical modeling quite complex. The physically based classical models require wide range of parameters The study was carried out for the catchment of related to land uses, soil characteristics, soil horizon, Wainganga river in Godavari basin. The Wainganga is watershed treatment, manmade activities, conservation a river on Madhya Pradesh, which originates about 12 practices, soil moisture variation, topographic data, km from Mundara village of Seoni district in the southern surface roughness, etc. These parameters vary slopes of the Satpura Range of Madhya Pradesh. The significantly over a space and time, and are difficult to river flows in south direction through Madhya Pradesh monitor. Under these circumstances, classical models and Maharashtra. After joining the Wardha, the united require assumption of the parameters. Therefore a stream, known as the Pranahita, ultimately falls into the model with one or two parameters is desired for River Godavari. The stream flow model has been estimation of runoff. developed based on the stream flow data recorded at Kumhari gauging station near Balaghat, Madhya An artificial neural network (ANN), usually called
210 Pradesh. Kumhari is located at Latitude of N variables are listed (Table 1). In order to optimize the 21o52’58”and Longitude of E 80o10’41” on Balaght- number of input variables, stepwise regression analysis Kumhari road. The total catchment area at Kumhari is was also performed. Critical p values (p=0.05) was used 8070 sq km. to control entry and removal of effects from the model in stepwise regression. Various models (M-I to M-XV) were Daily stream flow data (from 1st June 1999 to 31st calibrated using 60 percent of the total data set and May 2008) of Kumhari gauging station (code AGH40R6) validated with remaining 40 percent of data. were collected from Central Water Commission, Nagpur. The daily rainfall data of Balaghat, Seoni and Keolari for the same period were obtained from the Office, Sub Artificial Neural Network (ANN) Divisional Officer, Left Bank Dhooti Canal, Subdivision no.6, Balaghat, Block Development Office Seoni and An ANN is a massively parallel-distributed information Block Development Office, Keolari, respectively. processing system that has certain performance characteristics resembling biological neural networks of Development of Multiple Linear Regression Model the human brain (Haykin 1994). ANN is a network of large number of processing elements (also called as nodes or artificial neurons) connected by unidirectional Rainfall is the important input variable for runoff communication channels (connections), each with its modeling, which varies with time and space over a own small sphere of knowledge and access to data in catchment. Therefore, rainfall for prediction of runoff on its local memory. Typically, a neural network is tth day (Q ), rainfall recorded on tth day at the Balaghat t characterised by its architecture that represents the (PB ), Seoni (PS ) and Keolari (PK) has been considered t t t pattern of connection between neurons, its method of as input variable. Since the catchment area is large, determining the connection weights and the activation watershed storage shall affect the streamflow. To function (Fausett 1994). incorporate the effect of watershed storage, previous day’s rainfall (Pt-1), two days earlier rainfall (Pt-2); and The structure of an artificial neuron is shown in rd previous 3 day rainfall (Pt-3) of all the three stations have Fig. 1. There are weighted input connections to the been also included as input variables. During non-rainy artificial neuron, analogous to the dendrites. These input periods, some quantity of stream flow also has been signals get added up, and are fed into an activation recorded at the gauging station. This flow is base flow function, which determines if the neuron should at all and is assumed to be affected by previous day’s stream react like soma in biological neuron. The reaction signals flow. Stream flow on previous day (Qt-1), two days earlier of the neuron would then pass through a transfer
(Qt-2) and three days earlier (Qt-3) has been also function, which decides the strength of the output signal considered as input variable to develop the model. Multiple linear regression models developed with input y j f X W j bj
Table 1. Multiple linear regression models with varying input variables Model Output Input Variable Variable
M-I Qt PBt
M-II Qt PBt, PSt
M-III Qt PBt, PSt, PKt
M-IV Qt PBt, PSt, PKt, Qt-1
M-V Qt PBt, PSt, PKt, Qt-1, Qt-2
M-VI Qt PBt, PSt, PKt, Qt-1, Qt-2, Qt-3
M-VII Qt PBt, PSt, PKt, Qt-1, Qt-2, Qt-3, PBt-1
M-VIII Qt PBt, PSt, PKt, Qt-1, Qt-2, Qt-3, PBt-1,PBt-2
M-IX Qt PBt, PSt, PKt, Qt-1, Qt-2, Qt-3, PBt-1,PBt-2, PBt-3
M-X Qt PBt, PSt, PKt, Qt-1, Qt-2, Qt-3, PBt-1,PBt-2, PBt-3, PSt-1
M-XI Qt PBt, PSt, PKt, Qt-1, Qt-2, Qt-3, PBt-1,PBt-2, PBt-3, PSt-1, PSt-2
M-XII Qt PBt, PSt, PKt, Qt-1, Qt-2, Qt-3, PBt-1,PBt-2, PBt-3, PSt-1, PSt-2, PSt-3
M-XIII Qt PBt, PSt, PKt, Qt-1, Qt-2, Qt-3, PBt-1,PBt-2, PBt-3, PSt-1, PSt-2, PSt-3, PKt-1
M-XIV Qt PBt, PSt, PKt, Qt-1, Qt-2, Qt-3, PBt-1,PBt-2, PBt-3, PSt-1, PSt-2, PSt-3, PKt-1, PKt-2
M-XV Qt PBt, PSt, PKt, Qt-1, Qt-2, Qt-3, PBt-1,PBt-2, PBt-3, PSt-1, PSt-2, PSt-3, PKt-1, PKt-2, PKt-3
211 similar to hillock. Finally, the output signal is send through small scalar which controls the learning process, e is an all the output connections to other neurons as through error vector, DW is the change in weights of the synaptic synapses in case of biological neuron. connections between the layers, JT is the transpose of the Jacobian matrix and I is the identity matrix. Therefore, in LM algorithm, the value of m decreases after each Mathematically, the inputs to a neuron may come successful step and increases only when a step 1 increases the error. In practice LM is much faster and f (x) 1 exp x better for a variety of the problems (Coulibaly et al. 2000).
Normalization of data
Due to nature of the algorithm, large values slow training because the gradient of the sigmoid function at extreme values approximate zero. Also, because of the nature of the logistic activation function used in the output layer, outputs from the network are constrained to the range [0, 1]. In addition, because each predictor can cover a (a) Biological Neuron (b) Artificial Neuron different range of values, it is prudent to rescale each input to common range so that one predictor does not Fig. 1. An artificial neuron and biological neuron dominate all other. Mean and Standard Deviation showing its components (mapstd), an approach for scaling network inputs and targets was used to normalize the mean and standard deviation of the training set. The function mapstd from an input vector X = (x1, x2 ,….., xn). The sequence of weights leading to a neuron from a weight vector W = normalizes the inputs and targets so that they will have j zero mean and unity standard deviation. They effectively (w1j, w2j….wnj) where wij represents the connection weight from the ith neuron in the preceding layer to the jth become a part of the network, just like the network neuron. The output of the neuron j, yj is obtained by weights and biases. After that these outputs converted computing the value of function ¦ with respect to the inner back into the same units. A program was written in product of the vector X and Wj minus bj, where bj is the MATLAB 6.5; Neural Network Toolbox to design the ANN threshold value also called as bias associated with the architecture and for defining training algorithm, error goal neuron. In neural netwrok parlance, the bias bj must be etc. exceeded before it can be activated. The following equation defines the operation: Selection of input vector The function ¦ is called as an activation function. The activation function enables a network to map any The selection of an appropriate input vector that may non-linear process. The most commonly used activation allow an ANN to map the desired output is an important function is the sigmoidal function expressed as: prerequisite for successful application of artificial neural networks. Selection of key input parameters help in Learning of ANN avoiding loss of information, whereas inclusion of spurious variable tends to confuse the training process. Fifteen ANN models M1-1 to M-15 were developed with Levenberg-MarquardtW H (LM)I algorithm1 J T e was used for number of input variables 1 to 15 as listed in Table 1- learning of ANN. The second order non-linear Column 3. All ANNs were trained using supervised optimisation techniques used in the training is expressed training algorithms that tried to minimize a performance as: measure (often termed objective function from the point of view of validation). The Mean Squared Error (MSE) was considered as objective function of ANN. The ANN model architecture is single layer feed forward network, where H = JT * J, is the Hessian matrix of error vector which is the most commonly used neural network for and is equal to (Ñ2E @ 2 JT J), J=ÑE is the Jacobian the prediction of the non-linear process like rainfall-runoff matrix of derivatives of each error to each weight, m is a relationship. Initially 15 number of Neurons was considered in the development of ANN model. The
212 number of the hidden layer is one. The transfer function determination (R 2), were used to evaluate the from input to hidden layer is Tan-Sigmoid Transfer performance of the developed models. Function (Tansig) and from hidden layer to output layer is Linear Transfer function (Purelin). Results and Discussion
Selection of Number of Neurons Multiple Linear Regression model Number of neurons (N) selected for developing ANN Using 60 percent of data for calibration of model and 40 model is important. Lower N, results in poor training of percent data for validation of developed model, the model and by increasing N, training performance is regression models with varying number of input variables improved. With Higher values of number of neurons, have been developed. Constant and coefficients of generalization of the model is poor and reflected in model various models are given (Table 2). Graphical relation performance during model validation. Number of neurons between predicted and observed runoff during calibration was varied from 5 to 20 for the ANN model optimized and validation of model M-I is depicted in Fig.2., and for with number of input variables and final model was model M-XV in Fig.3. In general, by increasing the input selected with optimum number of neurons. All the ANN variables in the model, value of R2 has increased and models tested were trained with 60 percent of total data value of RMSE and MAE has decreased; showing the length and validated with remaining 40 percent data. improvement in the model efficiency.
Performance Indicators Stepwise Regression Analysis Increasing input variable from Model M-I to Model M- Mean squared error (MSE), Root mean squared error XV, indicate that inclusion of each input variable has (RMSE), Mean Absolute error (MAE), and Coefficient of increased the correlation coefficient. In some cases the
Fig.2 Relationship between observed and estimated stream flow during calibration and validation of the model M-I
Fig 3 Relationship between observed and estimated stream flow during calibration and validation of the model M- XV Multiple regression model M-XV over estimates the stream flow especially during the period of high flow (Fig.3).
213 Table 2. Constant, coefficient and performance of regression models Model Constant Coefficient R2 RMSE MAE R2- Validation
M-I 55.604 10.535 0.111 452 122 0.184 M-II 16.483 2.799, 23.116 0.281 419 114 0.280 M-III 5.276 -0.221, 10.942, 21.111 0.413 391 114 0.287 M-IV -21.699 -1.099, 6.231, 19.974, 0.468 0.614 321 96 0.452 M-V -17.441 -1.046, 6.080, 19.942, 0.522, -0.090 0.620 319 97 0.455 M-VI -22.871 -1.266, 6.181, 29.921, 0.537, -0.173, 0.126 0.630 316 96 0.460 M-VII -16.912 -0.76, 6.712, 20.553, 0.557, -0.174, 0.127, -2.666 0.635 319 97 0.432 M-VIII -23.412 -1.241, 6.946, 19.635, 0.554, -0.199, 10.128, -3.139, 3.026 0.642 317 90 0.430 M-IX -22.960 -1.216, 6.29, 19.646, 0.555, -0.199, 0.130, -3.121, 3.063, 0.642 317 89 0.431 -0.234 M-X -22.536 -1.229, 7.002, 19.744, 0.561, -0.201, 0.131, -3.008, 3.117, 0.643 317 89 0.429 -0.249, -0.633 M-XI -24.433 1.289, 6.734, 19.380, 0.555, -0.227, 0.136, -2.924, 2.478, 0.645 317 90 0.430 -0.560, -1.067, 3.603 M-XII -22.445 -1.103, 6.722, 19.436, 0.563, -0.225, 0.159, -2.964, 2.457, 0.647 317 89 0.421 -0.017, 0.896, 3.957, -2.910 M-XIII -21.807 -1.052, 6.236, 18.282, 0.476, -0.173, 0.153, -3.578, 2.088, 0.659 310 89 0.435 -0.186, -2.693, 3.162, -3.335, 7.688 M-IV -21.509 -0.994, 6.295, 18.246, 0.491, -0.140, 0.134, -3.579, 2.415, 0.661 309 89 0.442 -0.052, -2.596, 3.998, -2.976, 7.919, -3.465 M-XV -21.785 -0.984, 6.495, 18.230, 0.483, -0.123, 0.148, -3.564, 2.393, 0.663 309 89 0.443 -0.210, -2.544, 4.021, -2.268, 8.002, -3.248, -2.470
1.0E+04 input variables is at par with fifteen variables. The model Qto can be written as- 1.0E+03 Qtp
1.0E+02 Qt = – 21.301+5.795PSt + 14.251PKt + 0.423Qt-1 – 0.810Qt-2
+ 0.114Qt-3 – 0.821PBt-1 + 2.025PBt-2 – 0.170PSt-1 + 1.0E+01 2.093PSt-2 – 0.548PSt-3 + 6.897PKt-1 – 3.425PKt-2 – 1.825PK Stream Flow, Cumec Flow, Stream 1.0E+00 t-3
1.0E-01 ANN based model 0 500 1000 1500 2000 2500 3000 3500 Days ANN models have been developed with varying input
Fig.4. Hydrograph sowing observed (Q to) and variables, and evaluated in terms of performance predicted (Qtp) streamflow by MLR model with 13 input indicators. The ANN model M1 with one input variable variables achieved the error (MSE) of 0.895 and R=0.4626 and 2 increase in coefficient of determination (R ) is very small 0.36998 during model training and validation respectively or insignificant. To find out that which input variables (Fig.5). Performance parameters of ANN models with makes significant change in coefficient of determination various input variables have been listed in the Table 3. 2 (R ) and should remain in the model and to discard those As number of input variables increased from one to variables having insignificant influence on correlation fifteen in ANN model, training performance indicator, coefficient, stepwise regression analysis was carried out. MSE of scaled output and target has reduced from 0.895 In stepwise regression analysis, thirteen input variables to 0.125 (Table 3). Mean absolute error between estimate remained in the model and two variables (PBt and PBt-3) and target during training has reduced from 119 to 48; were removed. Performance parameter with stepwise and during validation from 94 to 56. regression analysis are- R2=0.662, RMSE=308, MAE=81.The R2 for the Model M-XV during training is Table 3 clearly indicates that amongst fifteen ANN 0.671 where as with stepwise regression the R2= 0.662. models with varying input variables developed, model It indicates that stepwise regression model with thirteen
214 M14 performed best in terms of R2, RMSE and MAE 15 neurons is depicted in Fig.7. Hence model M14 has during training as well as validation with independent been considered for further improvement in terms of data set. MSE during training process, relationship number of neurons. between observed and predicted stream flow with Model M14 is depicted in Fig.6. Hydrogaph of observed stream ANN models with different numbers of neurons flow and predicted stream flow with model M14 having
trainlm-M1 :std: 60-40:N=15 MSE= 0.89452
0
M.S.E. 10
0 2 4 6 8 10 12 Epoch
trainlm-M1 :std: 60-40:N=15Training R=0.4626 trainlm-M1 :std: 60-40:N=15 Validation=0.36998 18000 Data Point 16000 Data Point 8000 1:1 Line 1:1 Line 14000 12000 6000 10000 8000 4000 6000 Estimated Runoff, cumec Runoff, Estimated 4000 cumec Runoff, Estimated 2000 2000 0 0 0 2000 4000 6000 8000 10000 12000 14000 16000 18000 0 1000 2000 3000 4000 5000 6000 7000 8000 9000 Observed Runoff, cumec Observed Runoff, cumec Fig 4. Performance of Model M1 with N=15
trainlm-M14 :std: 60-40:N=15 MSE= 0.12474
Training Validation
0 10 M.S.E.
0 5 10 15 20 25 30 Epoch trainlm-M14 :std: 60-40:N=15Training R=0.93808 trainlm-M14 :std: 60-40:N=15 Validation=0.81741 18000 9000 Data Point 16000 8000 Data Point 1:1 Line 1:1 Line 14000 7000
12000 6000 10000 5000 8000 4000
6000 3000 Estimated Runoff,cumec Estimated Estimated Runoff, cumec Runoff, Estimated 4000 2000 2000 1000 0 0 2000 4000 6000 8000 10000 12000 14000 16000 18000 0 0 1000 2000 3000 4000 5000 6000 7000 8000 9000 Observed Runoff, cumec Observed Runoff, cumec Fig. 5. Performance of Model M14 with N=15
215 trainlm-M14 :std: 60-40:N=5 MSE= 0.44298 1.8 Training 1.6 Validation
1.4
1.2
1 M.S.E. 0.8
0.6
0.4
0.2 0 10 20 30 40 50 60 70 Epoch trainlm-M14 :std: 60-40:N=5Training R=0.81629 18000 trainlm-M14 :std: 60-40:N=5 Validation=0.67445 9000 16000 Data Point 8000 Data Point 14000 1:1 Line 1:1 Line 7000 12000 6000 10000 5000
8000 4000 6000 3000 Estimated Runoff,Estimated cumec 4000 Estimated Runoff,2000 cumec 2000 1000
0 0 0 2000 4000 6000 8000 10000 12000 14000 16000 18000 0 1000 2000 3000 4000 5000 6000 7000 8000 9000 Observed Runoff, cumec Observed Runoff, cumec Fig.8 Performance of Model M14 with N=6 trainlm-M14 :std: 60-40:N=7 MSE= 0.15766 1.5 Training Validation
1 M.S.E.
0.5
0 0 5 10 15 20 25 30 trainlm-M14 :std: 60-40:N=7Training R=0.93621 Epoch trainlm-M14 :std: 60-40:N=7 Validation=0.82837 18000 9000 Data Point Data Point 16000 8000 1:1 Line 1:1 Line 14000 7000
12000 6000
10000 5000
8000 4000
6000 3000 Estimated Runoff, cumec Runoff, Estimated 4000 Estimated Runoff, cumec 2000 2000 1000 0 0 0 2000 4000 6000 8000 10000 12000 14000 16000 18000 0 1000 2000 3000 4000 5000 6000 7000 8000 9000 Observed Runoff, cumec Observed Runoff, cumec Fig.9. Performance of Model M14 with N=7
216 An optimum number of neurons in the hidden layer of Table 4. Performance of Neural Network with different ANN model can be determined by trial and error, at which number of Neurons a model perform better. ANN Models with 14 input No. of R2 MSE RMSE MAE variables were further was tested with varying number Neurons Trg Val Trg Val Trg Val Trg Val of neurons (5 to 20). N5 0.666 0.454 0.443 0.358 334 301 57 60 MSE of scaled output and target is obtained as 0.443 N6 0.518 0.466 0.518 0.363 374 303 76 72 and 0.358 during training and validation, respectively; N7 0.876 0.687 0.158 0.207 196 230 43 49 coefficient of correlation between estimated runoff and N8 0.691 0.501 0.412 0.327 319 290 58 60 observed runoff is 0.816 and 0.674 during training and N10 0.691 0.483 0.354 0.343 305 296 59 64 N13 0.539 0.382 0.332 0.431 366 341 66 69 validation, respectively (Fig8) when 5 neurons were N15 0.880 0.667 0.125 0.222 189 234 48 56 N20 0.762 0.545 0.263 0.313 278 280 54 64
4 trainlm-M14 :std: 60-40:N=15 Hydrograph (Trg=Training Val=Validation) 10 Estimated Observed 3 of neurons is presented (Table 4). 10
2 Comparing the performance parameters for ANN model 10 with 14 input variables with various number of neuron
1 10 (Table 4), it can be stated that model performed better
0 with N=7 and N=15 then all others. Performance of ANN 10 Runoff, cumec model with 7 neurons is depicted in Fig.9. However, the
-1 10 overall performance in terms of R, MSE, RMSE and MAE (cumulative for training and validation) of model with N=7 -2 10 is best, model M14 with N=7 is found suitable for
-3 10 0 500 1000 1500 2000 2500 3000 3500 prediction of stream flow. Fig. 10 presents the Time, Days hydrograph of observed and estimated by M14 with N=7 Fig.7. Hydrograph showing observed and estimated stream flow with model M-14 (training with 60% data) model. A close agreement can be observed between estimated and target runoff. considered in the ANN. MSE during training has been reduced as compared to the earlier ANN model with N=15. Performance of ANN models with various number Based on the above findings following conclusions are drawn.
Table 3. Performance of Neural Network models with · Regression model with thirteen input variable is different input variables found most suitable for stream flow forecasting. Model R2 MSE RMSE MAE · The ANN model with Levenberg-Marquardt, 7 Trg Val Trg Val Trg Val Trg Val Numbers of neurons is found best model for runoff estimation. M1 0.214 0.144 0.895 0.566 478 395 119 94 M2 0.243 0.292 0.825 0.465 468 357 119 97 · ANN based model is superior to multiple linear M3 0.333 0.257 0.688 0.489 446 363 114 99 regression models for runoff forecasting. M4 0.596 0.503 0.370 0.339 348 300 60 57 M5 0.792 0.584 0.279 0.277 261 269 48 52 unh ds ty izokg dk iwokZuqeku] bathfu;fjax vkSj lalk/ku izca/k ds M6 0.789 0.566 0.136 0.287 270 275 53 58 M7 0.759 0.594 0.196 0.267 283 269 65 58 {ks= esa egRoiw.kZ gSa A cSuxaxk tyx`g.k {ks= ckyk?kkV ds utnhd M8 0.726 0.516 0.216 0.327 293 295 53 61 dqEgkjh ekiu LVs’ku ij] unh ds ty izokg ds iwokZuqeku ds fy, M9 0.843 0.545 0.163 0.321 238 282 50 60 ,dkf/kd js[kh; izrhixeu fo’ys'k.k ekWMy rFkk —f=e raf=dk usVodZ M10 0.639 0.320 0.274 0.488 357 339 70 65 M11 0.604 0.376 0.418 0.433 350 342 75 71 ekWMy fodkl ds fy, pquk x;k gS A mUuhl o"kksZ ds dqEgkjh ds nSfud M12 0.533 0.428 0.461 0.381 375 315 61 63 ty izokg rFkk ckyk?kkV] flouh ,oa dsoykjh ds nSfud o"kkZ dk M13 0.642 0.469 0.372 0.370 333 306 70 77 M14 0.880 0.667 0.125 0.222 189 234 48 56 mi;ksx ty izokg ekWMy fodflr djus ds fy;s fd;k x;k A ;g ik;k M15 0.599 0.517 0.490 0.342 355 290 69 69 x;k rsjg buiqV pj ds lkFk ,dkf/kd js[kh; izrhixeu ekWMy dk (Trg = Training, Val = Validation) izn’kZu csgrj gS A pkSng buiqV pj] lkr U;jkUl] ,d fNih ijr]
217 ,d vkmViqV ijr ds lkFk vkSj ysosucxZ&ekdZM~V ,yxksfjFe ds lkFk Deka P, Chandramouli V (2005) Fuzzy neural network model for hydrologic flow routing. J Hydrol Eng 10(4): 302– —f=e raf=dk usVodZ ekWMy dk izn’kZu loksZÙke gS A 14 Fausett L (1994) Fundamentals of neural networks. Prentice Hall Englewood Cliffs New Jersey References Haykin S (1994) Neural networks: a comprehensive foundation. Macmillan New York Cigizoglu HK (2003) Estimation, forecasting and extrapolation Kisi O (2004) River flow modeling using artificial neural of flow data by artificial neural networks. Hydrol Sci networks. ASCE J Hydrol Eng 9(1):60–63 J 48(3) 349–361 Kisi O (2008) River flow forecasting and estimation using Cigizoglu HK, Kisi O (2005) Flow prediction by three back different artificial neural network techniques. Hydrol propagation techniques using k-fold partitioning of Res 39(1):27-40 neural network training data. Nordic Hydrol 36(1) 49– Sarangi A, Madramootoo CA, Enright P, Prasher SO, Patel 64 RM (2005) Performance evaluation of ANN and Coulibaly P, Anctil F, Bobee B (1999) Daily reservoir inflow (Manuscriptgeomorphology-based Received: 27.05.2011; Accepted: models 30.08.2011) for runoff and forecasting using artificial neural network with sediment yield prediction for a Canadian watershed. stopped training approach, J Hydrol 230 244-257 Cur Sci 89(12): 25 December 2005 Sudheer KP, Jain SK (2003) Radial basis function neural network for modeling rating curves. ASCE J Hydrol Eng 8(3):161–164
218 JNKVV Res J 45(2): 219-222 (2011)
A multiple regression approach to predict production of paddy and wheat crops in Madhya Pradesh
K.S. Kushwaha and P.K. Awasthi* Department of Maths & Statistics Jawaharlal Nehru Krishi Vishwa Vidyalaya Jabalpur 482004 (MP) *Department of Agricultural Economics & Farm Management Jawaharlal Nehru Krishi Vishwa Vidyalaya Jabalpur 482004 (MP)
various experiments conducted. Abstract
Material and methods During the last few decades, the statisticians, economists & other scientists have given due consideration to see the performance of the production of paddy and wheat. crops Secondary data were collected from Agricultural based on area and fertilizer consumed. Multiple regression Statistics of M.P. State available in the Department of models have been fitted using least square technique. Agricultural Economics & Farm Management in JNKVV, Independence of serial (auto) correlation in error terms of the Jabalpur for 1981-82 to 2005-06. model and consistency for variation in observed and predicted production have also been considered. To perform the multiple regression analyses for the data available, we have denoed yield by(x1) as
response variable, and area by (x2), fertilizer consumed Keywords: Multiple regression, auto correlation, Durbin by (x3) as two covariates. Now, multiple regression model Watson ‘d’ statistics, partial regression coefficients. is postulated as
X = + X + X + e ... (1) Rice and wheat are the most important staple food crop 1 0 12.3 2 13.2 3 grown in all parts of India. India occupies largest area Where b0, b123 and b13.2 are unknown parameters to be (43 million ha.) under rice with production of (81 million estimated from available data itself and e is the random tones). The average productivity in India is 1.33 tones/ error term involved in the model and distributed as e ~ N hectare. 2 (o, se ). Using least square technique, the fitted In this study, the status of area, production and regression line is obtained as – productivity of Rice and wheat and projected levels of X = bo + b X + b X …(2) the crops in the years to come in M.P. state have been 1 12.3 2 13.2 3 considered.
where bo= X1 is the sample mean, and b12.3, b13.2 are Test of independence of autocorrelation in error the sample partial regression coefficients of X1 on X2 terms accruing in approach of multiple regression theory and X3 respectively. has been reported by (Durbin Watson 1951). An Cov(X 1, X 1) The multipleR correlation coefficient R1.23 between advantageous metrit of ‘d’ statistic is that it is based on 123 X , and joint effect of XV(X and1)V( XX 1on) X , is reported as estimated residuals obtained from regression study. The 1 2 3 1 .... (3) multiple regression models have been fitted for the data of 25 years on rise and wheat. Kulhare (2003), Rajput (2003), Shrivastava et al. (2003), Raghuwanshi et al. The test of significance of R123, the test statistic (2003), Sharma and Jain (1997), Sharma and Joshi (F statistic) is carried out as (1995) have made an attempt to study in the area through 2 R (n-k-1) 1.23
219 F = ---————— ————— …(4) (K, n-k-1) 2 x (1- R ) K C.V. (X) = ———— x 100 …(6) 1.23 where K=2
A test for independence of serial correlation in The R1.23 for paddy crop is computed as error terms based on residual ei. = (x1 – X1), (I =1,2,3, 2 2 2 2 ….., n) will be carried out using Durbin Watson ‘d’ statistic R 1.23 = (r 12 + r 13 -2 r12 r23 r31) / (1-r 23) defined as n 2 = 0.3644 (e -e ) i=2 i i-1 To test the significance of R i.e. Ho : R = 0 the test d = —————————— ...( 5) 1.23 1.23 n 2 statistic F is computed as e i=1 i R2 (n-k-1) F (K, n-k-1) = —————, ————— = 6.3065 with statistical hypothesis stated as – (1-R2) K
for k = 2, i.e. F(2, 22) = 6.3065 > F0.05 (2, 22) Ho : The error terms ei’s in regression equation are serially independent i.e. (uncorrelated). Hence, Ho is significant and conclude that the multiple
H1 : The error terms ei,s in regression equation are regression equation of production (X1) obtained on the serially dependent i.e. (correlated) basis of area (X2) and fertilize (X3) is the best fitted equation for the rice crop.
Consistency of variability in observed yield data have been worked out using coefficient of variation (C.V.) Results and Discussion defined as X Rice
Table 1. Estimate of yield and its residue for ‘d’ statistic 2 Year Production Area Fertilizer Y ei = (Y - Y ) (e1 - e) e1
(Y) (X1) (X2) 1981-82 1026 1553 1233 1052.932 -26.9317 725.3165 1982-83 1021 1544 1542 1067.734 -46.7337 -19.802 2184.039 1983-84 1308 1587 1515 1125.569 182.4311 229.1648 33281.11 1984-85 761 1565 1751 1115.687 -354.687 -537.118 125802.7 1985-86 1396 1604 1969 1189.651 206.3494 561.0361 42580.07 1986-87 976 1591 1962 1170.821 -194.821 -401.17 37955.07 1987-88 974 1418 1689 904.2844 69.7156 264.5362 4860.265 1988-89 1292 1525 2335 1111.46 180.5405 110.8249 32594.87 1989-90 1036 1500 2398 1082.03 -46.0301 -226.571 2118.77 1990-91 1435 1556 2468 1166.678 268.3217 314.3518 71996.53 1991-92 978 1559 2877 1207.159 -229.159 -497.48 52513.71 1992-93 1185 1573 2959 1234.042 -49.0419 180.1168 2405.108 1993-94 1346 1566 3064 1300.644 158.3563 45.9068 25076.72 1994-95 1459 1612 3094 1300.644 -172.242 45.9068 25076.72 1995-96 1212 1672 3089 1384.242 -0.8853 -330.599 29667.38 1996-97 1346 1644 3110 1346.885 -135.669 171.3569 0.783756 1997-98 1229 1656 3121 1364.669 36.5646 134.784 18406.19 1998-99 1424 1672 3125 1387.435 265.1882 172.234 1336.97 1999-00 1750 1740 3149 1484.812 -459.675 228.6236 70324.78 2000-01 982 1708 3168 1441.675 155.989 -724.863 211300.8 2001-02 1693 1776 3169 1537.011 -375.759 615.6637 24332.57 2002-03 1032 1681 3212 1407.759 288.3939 -531.748 141194.5 2003-04 1750 1719 3219 1461.606 -107.181 664.1525 83171.04 2004-05 1309 1686 3228 1416.181 274.5146 -395.575 11487.83 2005-06 1656 1658 3279 1381.485 -0.0005 381.6959 75358.27 Sum 31576 10365 65725 301.4463 1113320
220 For estimating the value of production of paddy crop, X = -5596.68 + 2.6252 X + 0.4608 X 1w 2 3 the following regression line has been obtained
Durbin – Watson d statistic for production of wheat XIp = -1231.7225 + 1.4007 X2 + 0.0887 X3 R for wheat crop is computed and is obtained as Here K=2 , n =25, and from the statistical table of drapper 1.23 and smith (1966), we have the values of dl and di at 5% 2 level of significance for two tail test given as R 1.23 = 0.9504 and for testing of significance of R1.23 i.e. Ho: R1.23 =0, the value of F statstic is obtained as earlier and we have dl = 1.18, (4-dl) = 2.82 du = 1.34, (4-du) = 2.66 F = 210.7742 (2,22) The computed value of durbin –watson ‘d’ statistic from table 2, is obtained as : d=0.019937 Which is > tabulated F0.05 (2,22). Thus, it is concluded that the multiple regression equation of production (X ) obtained on the basis of area Thus, the computed value of ‘d’ statistic is equal to 1 0.019937 which is smaller than dl (1.18), Hence, the null (X2) and fertilizer (X3) is the best fitted equation for rice crop. hypothesis Ho is declared significant i.e. it is rejected and we finally conclude that the errors involved in the estimation of production of wheat crop for different years Wheat (1981-82 to 2005-06) are serially dependent.
For estimating the production of wheat crop, the following fiNys nl lkyks esa lkf[;dhfon] vFkZ’kkL=h; ,oa vU; cSKkfudksa us regression line –was obtained /kku ,oa xsgwW ds {ks=Qy ,oa mi;ksx esa ykbZ x;h [kkn ds vk/kkj ij muds iSnkokj ds ifj.kkeksa ij fo’ks"k /;ku fn;k gS A U;wure oxZ Table 2. Estimate of yield and its residuals for ‘d’ statistic 2 Year Production Area Fertilizer Y ei = (Y - Y ) (e1 - e) e1
(Y) (X1) (X2) 1981-82 3234 3204 1515 3512.573 -278.5728 77602.805 1982-83 3731 3446 943 3884.294 -153.2936 125.2792 23498.928 1983-84 4606 3675 1445 4716.786 -110.786 42.5076 12273.538 1984-85 3856 3499 1598 4325.253 -469.2532 -358.467 220198.57 1985-86 4136 3592 1827 4674.92 -538.92 -69.6668 290434.77 1986-87 4161 3381 1700 4062.481 98.5188 637.4388 9705.954 1987-88 4546 3532 1719 4467.642 78.3584 -20.1064 6140.0989 1988-89 4724 3568 1928 4658.456 65.544 -12.8144 4296.0159 1989-90 4049 3196 1860 3650.547 398.4528 332.9088 158764.63 1990-91 5742 3738 1969 5123.633 618.3672 219.9144 382377.99 1991-92 5066 3458 2368 4572.436 493.564 -124.803 243605.42 1992-93 5172 3589 2485 4970.251 201.7492 -291.815 40702.74 1993-94 6675 4053 3970 6872.632 -197.6316 -399.381 39058.249 1994-95 7269 4096 3987 6993.349 275.6512 473.2828 75983.584 1995-96 6571 3925 3994 6547.665 23.3348 -252.316 544.51289 1996-97 7701 4235 3980 7355.026 345.974 322.6392 119698.01 1997-98 7154 4502 3990 8060.562 -906.5624 -1252.54 821855.39 1998-99 8257 4575 3997 8255.428 1.5724 908.138 2.4724418 1999-00 8687 4670 4013 8512.194 174.8056 173.2332 30556.998 2000-01 4869 3311 4019 4947.312 -78.3124 -253.118 6132.832 2001-02 6001 3704 4020 5979.477 21.5232 99.8356 463.21814 2002-03 4623 3382 4035 5141.074 -518.0744 -539.598 268401.08 2003-04 7365 4046 4062 6894.024 470.9764 989.0508 221818.77 2004-05 7327 4200 4087 7312.45 14.5504 -456.426 211.71414 2005-06 5958 3693 4105 5989.768 -31.7676 -46.318 1009.1804 Total 141480 94269 73616 -0.2316 246.8052 3055337.4
221 Table 3. Consistency for variation in observed production statistics 2nd edition : CV (Xp) and CV (Xw) in paddy and wheat crops during Durbin J, Watson GS (1951) Testing for serial correlation I the periods (1981-82 to 2005-06) are given below least square regression Biometrika 38 159-171 Kulhare PS (2003) Effect of farm yard manure and fertility Paddy production Wheat production levels on wheat (Triticum aestivum) in Tawa Mean 1263.04 5659.2 cammand area Bhartiya Krishi Anusandhan Patrika SEm± 50.0309 311.4414 18 (3/4) 114-18 Karnal R2 03644 0.9504 Raghuwanshi RS, Thakur P (2003) Economic performance CV (X1) 3.96% 5.50 of fertilizer on rabi crops Research Crops 4(1)135- 37 Rajput RL (2003) Effect of different levels of fertilizer on yield izfØ;k dk mi;ksx djrs gq, cgqmnns’kh; izlj.k Qyuksa dk izfriknu of wheat under chambal command area Bhartiya fd;k x;k gS A Qyu esa fy;s x;s =qfV ds ewY;ksa esas vkus okys Lor% Anusandhan Patrika 18 (3/4) 169-71 lglEc/kksa dh lorU=rk ,oa voyksfdr vkSj vuqekfur mit esa vkus Srivastava UK, Rajput RL (2003) Effect of different fertilizer levels and zinc on grain yield of wheat Bhartia Krishi okys vUrj ds lkeUtL; dks Hkh /;ku esa j[kk x;k gSA Anushandhan Patrika 18 (1/2) 58-60 Sharma RS, Jain KK (1997) Agronomic research advances in rice-wheat system in Madhya Pradesh Adv in Agric References (ManuscriptRes Received: in India 27.05.2011; (7 (4) 139-57 Accepted: 30.08.2011) Sharma VP, Joshi DK (1995) Performance of rice production Drapper NR, Smith HC (1966) Applied Regression Analysis, and factor affecting acreage under rice in costal Villey Series in Probability and mathematical regions of India Indian. J Agril Econ 50(2) 153-67
222 JNKVV Res J 45(2): 223-227 (2011)
Jack- knife technique and almost unbiased class of ratio type estimators in circular systematic sampling scheme
K.S. Kushwaha Department of Mathematics and Statistics Jawaharlal Nehru Krishi Vishwa Vidyalaya Jabalpur 482004 (MP)
Abstract using CSS scheme are as follows.
A general class of almost unbiased ratio type estimators i. Select a random number ‘r’ from (1 to N) and name
TRU to estimate the population mean of response variatey it as ‘random start’. in (C.S.S.) scheme is hereby proposed based upon Jack- knife technique. The explicit expression for the sampling ii. Choose some integer value of K = (N/n) or number nearest to (N/n) and name it as skip. variance of the class is derived to the terms of order 0(n-1). Minimum variance unbiased estimator (M.V.U.E.) in the class iii. Select all the units in the sample with serial numbers is investigated. An empirical illustration is provided to examine the utility of the results derived. r + jk if (r+jk) N, {j = 0,1,2……., (n-1)}, 1 r N
Key words: Ratio estimator, jack-knife technique, circular r + jk-N if (r+jk) > N, {j = 0,1,2……., ( n-1)}, 1 r N systematic sampling, M.V.U.E. (estimator). The classical ratio estimator under Linear Systematic Sudhakar (1978) pointed out that the use of skip Sampling (LSS) was proposed by Swan (1964) and its or span of sampling as an integer nearest to (N/n) in properties were studied. In general, the ratio estimator CSS does not always provides a sample of desired size is biased, A weighted class of ratio type of estimators n. Sudhakar (1978) has also mentioned that if the span was proposed and made almost unbiased by Kushwaha of sampling ‘K’ is the nearest integer £ N/n, then we do and Singh (1989) using Jack-Knife technique in LSS not encounter the above cited difficulty although this scheme. choice depends upon n. A serious demerit of LSS scheme is that it is not J ack-knife technique has been profitably employed possible to estimate the sampling variance of the in several estimation and testing problems. In this study we have utilized jack-knife technique to get rid off bias estimator under study but using the technique of in usual ratio estimator proposed in CSS scheme and interpenetrating systematic sampling with independent have derived a general class of almost unbiased ratio random start, it can be estimated unbiasedly. A simple type estimators say T modification of LSS scheme makes it possible to ensure RU. a fixed sample of size n and to make the sample mean`y An expression for sampling variance (TRU) along with an empirical illustration is provided to see the unbiased for population mean`yN even in case of N¹K.n where K is a positive integer. This sampling scheme is performance of the derived estimators (TRU) with respect known as ‘Circular Systematic Sampling’ (CSS) scheme. to efficiency point of views over other estimators existing in literature. Murthy (1977) and Sukhatme and Sukhatme (1970) have suggested to use CSS scheme in situation when N¹Kn. Class of Almost Unbiased Ratio Type Estimators The main steps involved in selecting a sample Let the population be consists of N units.U=(U1, U2,
223 th ……………..UN) labeled from (1 to N) in circular order. each of size m’= (n-m) units obtained by omitting t (t = let N¹Kn where k is an positive integer nearest to (N/n). 1,2,…,g) subsamples from complete circular systematic We select one sample at random and observe both the sample of size n. variates (y,x) for each and every unit included in the The estimators (`y ', `x ') are unbiased for (`Y, `X) sample. t t y t ' respectively. Withy R tthis' background,X and we define another ratio type estimator alongx t ' with its Jack-knife version Let ( yr jk xr jk j = 0, 1, ….., (n -1) and r = 1, 2, …….., N) written as g be the sample units. 1 y'R y'R t g t 1 (2.4) Circular systematic1 n 1 sample means (y, x) are defined y y r jk as n j 0 Y 2 - 1 n 1 The expressionB1(y R ) for mbias1 bofn ( ,1 )g to wther(1 termsk ) ofC xorder 0(n j = 0,1,2,...... , (n-1) 1 x xr jk ), are respectivelyn given as n j 0 Y * 2 and . B1(y R ) mg bn gg wr(1 k )Cx (2.5) r = 1,2,3,...... , N (2.1) n
K = Sampling span where, Y R v ( x) c o v ( y, x) U The sample means (y, x) are respectively unbiased B ( y ) 1 R S 2 V n T X Y , X W estimators of the population means (Y,X) . The 1 N population means`X of covariate x is assumed to be V( y ) ( y, Y ) 2 known in prior. N r 1
The classical ratio estimator `y of`Y based on a 2 R 1 N F 1 n 1 I y Y sample in CSS [ yr jk , J= 0,1,…....y (n-1)] of size n is G r Jk J y X N r 1 H n j 0 K defined as R x
2 1 F n 1 I (2.2) y nY 2 G r Jk J Nn H j 0 K Jack-knife technique is employed to reduce the bias (`y ). Let N¹Kn and split the selected sample in CSS in N n-1 N R 1 L 2 O to g sub samples each of size m units in a systematic yr+Jk nY (yr Jk nY)(yr J'k nY 2 Mr 1j 0d i j 0 j J P manner as this avoids the need for selecting the samples Nn N Q in the form of sub samples of smaller size (m < n), thereby retaining the efficiency generally obtained by a large 1 L N n-1 2 N N O circular systematic sample. Let (`y , `x , t=1,2…, g) be 2 dyr Jk nYi (yr Jk ny)(yr J'k ny t t Nn Mr 1j 0 r 1j 0 j J P unbiased estimators of (`Y,`X) based on circular N Q systematic sub samples each of size m. Let us define complimentary sub sample means. (`y ', `x ', t = 1 t t Nn 2 Nn(n 1) 2 1,2,….....g) written as Nn2 y yy y 2 y' = (n y - m y)/m', t' = 1,2,3 ... g y t t n1 (n 1) yws x ' = (n x - m x )/m' (2.3) t t Similarly
2 which are the sample means based on subsamples. x v(x) n1 (n 1) yws n
224 By definition, the intraclass correlation coefficient r yw which can be proved easily. If we take , wihtin the sample of the same variate (say y) is defined 1 2 as - and 3 (1 ) , the unbiasedness condition (2.7) reduces to- E(yr Jk Y )(yr J 'k Y ) yw 2 E(yr Jk Y ) = -(1- )/(h-1)
N n 1 n 1 where a, b are the suitably chosen constants . Thus, ( yr jk Y )( yr J 'k Y ) we obtain a general class T of almost unbiased ratio r 1 j 0 j J ' RU 2 type estimators in CSS scheme defined as Nn(n 1) y
1 1 (2.8) E(yr Jk Y )(xr Jk X ) TRU y b h 1 gyR ' b h 1 ghyR 0 yx 2 2 E(yr Jk Y ) E(xk Jk X )
3. Properties of Class TR E(y Y )(x X ) Cov(y, x) r Jk r Jk The expression for sampling variance of the proposed 2 2 E(yr Jk Y ) E(xr Jk X ) class TR in (2.6) can be written as
2 2 2 V (TR ) 1 v(y) 2v(yr0 ') 3v(yR ) 2 1 2 Sz * Cy Cz ,(z x, y),k yx Z Cx Cov(y, yR0 ') 2 3 1 cov(yr , y) ...(3.1)
To the terms of order O(n-1)the variances and covariances Here yx is the population correlation coefficient and for various estimators in (3.1) are cited in the lemma (3.1) given as- yx xw w is the intraclass correlation coefficient for the variates (y, x) and has been assumed to be same Y 2 (Murthy 1977), (Cx,Cy) are the C.V’s for (x,y) 2 V (y) l1 (n 1) wqCy respectively. n
Now taking the linear combination of (y, yR , yR ') , we V (yR0 ') v(yR ) cov(yR0 ', yR ) propose a weighted class of estimator TR as 2 3 Y 2 * 2 l1 (n 1) wq(Cy (1 2k )Cx ) TR 1 y 2 yR ' 3 yR , i 1, ...(2.6) n i 1
Cov(y, yR0 ') cov(y, yR ) where i ' s,(i 1,2,3) are suitably chosen weights 2 attached to different estimators . Now we have the Y 2 * 2 l1 (n 1) w q(C y k C x ) following theorem n
Theorem (2.1): The weighted class TR of estimators defined in (2.6) would be unbiased if- The results reported in lemma (3.1) can be proved easily by following the standard technique reported by h + = 0 Sukhatme and Sukhatme ( 1970) under the large 2 3 -1 sample approximation to the terms of order o(n ) under CSS scheme. g (n g) w for h = (2.7) [1 (n 1) w ]
225 Remark 3.1 Substituting the results (3.2) and 1 , 2 and (1 ) , in (3.1) and simplifying, we get 3 The variance of any estimator derived from the class the expression v(TRU) given as T RU (2.8) can be obtained by substituting the
2 corresponding values of constant á in V(TRU) cited in Y 2 * 2 (3.3). V(TRU) l1 (n 1) wqCy (1 ){(1 ) 2k }Cx ...(3.3) n Numerical Illustration The v (TRU) in (3.3) will be minimum for
To see the performance of the class T , we = (1 - k* ) = (say) ...(3.4) RU consider the data on y : the timber value and x: the strip length in strip wise complete enumeration.The 25 values
Thus, the resulting minimum V(TRU) is are considered from Murthy (1977) and treated as a population of size N= 25.The summarized data are 2 complied as follows. Y 2 *2 2 ...(3.5) MinV (TRU ) l1 (n 1) wqdCy k Cx i n 2 N=25 Cy =0.4 yx =0.08072 which is equivalent to the approximate variance of usual =459.84 2 =0.0153 K*=1.306 Cx biased linear regression estimator (ylrc ) in circular Y systematic sampling scheme written as =10.44 S =104.19 =0.0135 X xy w
The interclass correlation coefficient has been worked y y B ( X x) ...(3.6) 2 lrc yx out as n 1 x w (n 1) 2 where byx is the sample regression coefficient of (y on x) under CSS scheme. Substituting * * a n d 1 0 (1 k ), 2 k / (h 1) * in (3.1), we obtain Values of V1 (.) and percent relative 3 (1 0 0 ) k h / (h 1) Table 1. efficiency (. ,) an optimum estimator TRU0 in the class written as y Values of a Estimators V1(.) PRE (., y ) * k * k *h TRU 0 (1 K )y d h 1iyR0 ' d h 1iyR ...(3.7) - y 29.9x10-3 100.00
-3 a0 = -0.306 TRU0 8.6 x10 347.67 with V (TRU0) given in (3.5).
(1-2k*) -3 either 0 < < ( 1 - 2k*) - y R or y R0’ 78.3 x10 38.18 2(1-k*)<<0 T 226 where and are the population and within sample variance vuqfØ;k pj ds tula[;k ek/; dks vkdfyr djus ds fy;s vuqikfrr respectively defined under CSS scheme as izdkj ds yxHkx iw.kZ :Ik ls vufHkur vkdydksa ds c`gr lewg dks tSd&ukbQ izfØ;k ds vk/kkj ij izLrkfor fd;k x;k gSA lewg ds N n 1 2 1 2 -1 ( yr Jk Y ) izfrp;u izlj.k ds fuf’pr lw= dks 0(n ) ds in rd izkIr fd;k N r 1 J 0 x;k gSA lewg es de ls de izlj.k okys vufHkur vkdyu dk irk N N n 1 yxk;k x;k gSA ikzIr fd;s x;s lw=ksa ¼Qyuksa½ ds mi;ksfxrk dh tkWp 2 1 2 1 1 2 w rw (yr jk Yr ) djus ds fy;s ,d vuqHkfod mnkgj.k dks izLrqr fd;k x;k gSA N r 1 N r 1 n J 0 We have worked out the values of References V(T ) V (.) RU [C2 (1 ){(1 ) 2k*}C2] Kushwaha K S, Singh H P (1989) Class of almost unbiased 1 Y 2 y x ratio and product type estimator in systematic n {1 (n 1) w} sampling. J Indian Soc Agric Statistics (2): 193-205 Murthy M N (1977) Sampling theory and methods. Statistical and P.R.E. (. ,) = x 100, the percent y V (y) /V (TRU ) Pub Soc Calcutta India relative efficiency of proposed class with respect to y Quenouille M H (1949) Approximation test of correction in for different values of a and are complied in the Table 1. time series. J Roy Stat Soc 11: 68-84 Quenouille M H (1956) Notes in bias in estimation. Biometrica The relative efficiency of the estimator TRU is maximum among estimators considered here which 43: 353-360 shows that T (Table 1) is the most efficient (optimum) RU0 Sudhakar K (1978) A note on Circular Systematic Sampling estimator in the class TRU0 In practice, one may substitute Design. Sankhya Indian J Stat 40 (C1): 72-73 the estimated values of variances and covariance in order to obtain a “near optimum” values ofFor the choice Sukhatame P V, Sukhatame B V (1970) Sampling theory of of in an interval (-1.612<<1.0), the class T is always surveys with applications. lowa State Unive Press RU0 Ames lowa USA more efficient than the sample mean . It is also evident that the values of in the subinterval (-0.612<<0), the Swan A K P C (1964) The use of systematic sampling in ratio estimate. J Ind Stat Assoc 2 :160-164 class TRU is more efficient than y but is more efficient than the ratio estimator yx . (Manuscript Received : 12.01.2011; Accepted 30.05.2011) 227 JNKVV Res J 45(2): 228-230 (2011) Analysis and estimation of soybean yield using ANCOVA technique K.S. Kushwaha, K.K. Agrawal* and Sanjay Jain* Department of Mathematics and Statistics Jawaharlal Nehru Krishi Vishwa Vidyalaya Jabalpur 482004 (MP) *Department of Physics and Agrometeorology C.A.E., Jawaharlal Nehru Krishi Vishwa Vidyalaya Jabalpur 482004 (MP) Abstract the response variable. ANCOVA makes use of regression analysis under the role of linear prediction theory. Analysis of Covariance (ANCOVA) technique has been used to sort out the covariance effects assignable to different Keeping these facts in view, the present sources under investigation of soybean crop. Analysis reveals investigation was carried out to (1) Evaluate the effect that the yield attributing characters of soybean viz. seed weight of important characters which actually act as covariates plant-1, number of branch plant-1 and number of pods plant-1 have been found significant cofactors (covariates) in in soybean yield and (2) to estimate the soybean yield increasing the yield of soybean crop. under “unadjusted and adjusted effects of covariates” and test of significant difference among treatments mean (i.e. different date of sowing) effects on yield parameters. Keywords: Analysis of covariance techniques, Analysis of Variance techniques, Regression estimates Methodology for Statistical Analysis Globally, India ranked 5th in area and production of soybean. At present, the crop occupies an area of 6.5 Experimental data recorded on yield attributing million hectares with an annual production of 7.5 million characteristics on soybean crop viz. seed weight tones in India. Cultivation of soybean crop is plant-1, number of pods plant-1, number of seed plant-1 concentrated in central India which is predominantly in and number of branch plant-1 etc. have been noted from Madhya Pradesh, Maharastra and Rajasthan. Madhya the field experiment conducted during the year 2006- Pradesh contributes to the extent of 70% to area and 07, Department of Physics and Agrometeorology, 65% to its production in country and is often referred as College of Agricultural Engineering, Jawaharlal Nehru “Soybean State” (Kumar et al. 2008) Krishi Vishwa Vidyalaya, Jabalpur. As in ANCOVA techniques, we sort out the Following Steel and Torrie (1980), the linear variance components attributable to different sources statistical model in ‘Randomized Complete Block Design of variation like blocks, treatments, errors etc. ANCOVA (RCBD) for ANCOVA, is written as technique is the combination of two important techniques known as “Analysis of Variance” and “Regression yij ti j (xij x) ...(i) Analysis”. The regression analysis is used to analyze data obtained from “served data or observational data” but ANCOVA technique is utilized to analyze data where (i = 1, 2, 3……..t, j = 1, 2, 3……..r) th obtained from an experimental design. An ANCOVA (yij,xij):are the values (yield) on i treatments of soybean technique keeps statistical control of variability whereas and jth value of ith character of covariate, respectively an experiment can not control it effectively. This technique reduces the experimental error by taking (x.,. ):Grand mean of covariate X and general mean advantage of covariates which are highly correlated with of response variate y (yield) 228 th th (ti, ): Effect of i treatment and j block, respectively pair wise (Table 1.). j (b,e ): Regression coefficient of y on x and error terms ij The block effects for seed weight plant-1 and 2 -1 distributed as eij ~ N (0, e ) number of pods plant are non significant whereas the number of branch plant-1 is found to be any how ti yi y.. b(xi. x..) significant at 5% level of significance. The data in table also shows that four treatments (i.e. four dates of sowing y y.. b(x x..) for soybean crop, JS-335) are found highly significant at i . j j 5% level of significance for all the yield attributing characters (Table 2). b Exy / Exx Seed weight plant-1 shows that on comparison of 2 2 x.y sy.x Eyy Eyy / Exx ...(ii) adjusted mean yield, with critical difference values, it is clear that the timely date of sowing (i.e. treatment T2) is effective to give highest seed weight (yield) among the Here, E , E and E and are adjusted SS and xx xy yy treatments followed by treatment T3 and significantly SP for error, respectively and fe is the error d.f. The different from other treatments i.e. dates of sowing (Table deviation of any treatment mean from general mean must 3). be adjusted by the quantity b . This adjustment (xi. x..) The branches plant-1 revels that comparison of removes any attributable effect of covariate X on adjusted mean yield, with critical difference values, the response variate Y. It is the adjusted treatment means timely date of sowing (T2) is effective to give highest that are comparable effectively. seed weight (yield) among treatments followed by Pair wise correlation coefficient was worked out treatment T1 and is significantly different from other date for combination of all characteristics under study using of sowing. The dates (3rd and 4th) are pair wise the expression. significantly at par (Table 3). The number of pods plant-1 also shows that comparison of adjusted mean yield, with n n n x i y i x i y i / n critical difference value, the timely dates of sowing is i ...(iii) rxy effective to give highest seed weight (yield) among other n n 2 n n 2 F 2 IF 2 I dates of sowing followed by T and is significantly x F x I / n y F y I / n 1 HG i H i K KJHG i H i K KJ different from other date of sowing. It was concluded that the timely date of sowing Results and Discussion i.e. 3rd date of sowing i.e. 3rd July, 2007 is more effective among all date of sowing for kharif season crop to The Response variate Y (yield) is not correlated increase the yield. significantly with covariate i.e. x1 (number of branches -1 -1 plant ), x2 (number of pods plant ), x3 (number of seed -1 -1 Lk¨;kchu ij fd;s x;s ijh{k.k ds nkSjku ANCOVA rduhdh ds plant ), x4 (seed weight plant ) and x5 (number of seeds -1 covariance pod ), respectively. But all the five covariates (x1, x2, x3, }kjk ds fofHkUu ?kVdks dk v/;u fd;k x;kA lks;kchu x x ) are correlated significantly among themselves but 4, 5 ds fofHkUu y{k.kksa ds fo’ys'k.k ds i’pkr cht Hkkj izfr ikS/kk] 'kk[kk la[;k izfr ikS/kk ,oa Qyh la[;k izfr ikS/kk lks;kchu dh mit esa o`f) Table 1. The correlation matrix of various covariates with djus okys lkFkZd ,oa izHkkoh la?kVd (covariate) ik,s x;sA response variate (yield) Y X X X X X 1 2 3 4 5 Reference Y 1.000 X 0.562 1.000 Kumar VA, Pandey V, Shekh, AM, Kumar M (2008) Growth 1 X 0.472 0.992** 1.000 and Yield Response of Soybean (Glycine max L.) 2 X 0.428 0.984* 0.999** 1.000 In Relation to Temperature, Photoperiod and 3 Sunshine Duration, India. American Europian J X 0.446 0.988* 1.000** 1.000* 4 Agron 1 (2): 45-50, 2008 X 0.300 0.965* 0.982* 0.898* 0.897* 1.000 5 229 Table 2. Analysis of variance for unadjusted yield of soybean with different covariance separately Response covariates Source of variation d.f. Sum of Square Mean Sum of square Fcal. Ftab. Seed weight/plant Replication 2 435.526 467.760 0.878 5.14 Treatment 3 676520.600 225506.90 80.26* 4.76 Error 6 16858.920 2809.819 Total 11 698315.046 Number of branches/plant Replication 2 1.1267 0.5633 6.035 5.14 Treatment 3 45.2625 15.0875 161.65* 4.76 Error 6 0.5600 0.0933 Total 11 46.9492 Number of pods/plant Replication 2 6.660 3.330 3.85 5.14 Treatment 3 597.109 199.036 229.95* 4.76 Error 6 5.193 0.866 Total 11 608.963 Table 3. ANCOVA for adjusted and unadjusted yield of soybean with different covariance separately Covariance Treatments Average yield unadjusted Average yield adjusted Seed weight/plant T1 (0.03) 744.152 745.87 T2 (3.34) 1003.66 1006.53** T3 (3.01) 679.824 881.43 T4 (0.91) 337.72 331.54 Number of branches/plant T1 (14.24) 744.152 828.88 T2 (15.64) 1003.66 1292.88** T3 (14.34) 679.824 779.16 T4 (10.44) 337.72 132.63 Number of pods/plant T1 (20.1) 744.152 726.237 T2 (22.9) 1003.66 969.426* T3 (20.13) 679.824 661.734 T4 (4.97) 337.72 467.953 Steel RGD, Torrie JH (1980) Principles and Procedures of Wang L, Akvitas MG (2006) Testing for Covariance Effects in Statistics. A Biometrical Approach. 2nd Ed McGraw the Fully Non Parametric Analysis of Covariance Hill Kogarkusha Ltd Model. J American Stat Asso 101(474): 722-736 Uchikawa O, Fukushima Y and Matsue Y (2003) Statistical Wu-Jixiong (1995) Analysis of Variance and Covariance for Analysis of Soybean Yield and Meteorological Agronomic Traits with Unbalance Data in Hybrid of Conditions in the Northern Kyushu. Japanese J Crop Indian Rice. Chinese Rice Research News Letter Sci 72(2): 203-209 3(4): 10-11 (Manuscript Received: 22.01.2011; Accepted: 30.06.2011) 230 JNKVV Res J 45(2): 231-234 (2011) Impact of integrated application of organic manure and chemical fertilizers on productivity of soybean, wheat and chickpea grown on vertisols of Madhya Pradesh R.K. Bhatnagar, A.K. Dwivedi*, B. Sachidanand** and D.K. Pahalwan*** Department of Soil Science and Agricultural Chemistry College of Agriculture, Ganj Basoda Jawaharlal Nehru Krishi Vishwa Vidyalaya, Jabalpur 482004 (MP) *Directorate of Research Services Jawaharlal Nehru Krishi Vishwa Vidyalaya, Jabalpur 482004 (MP) **Department of Soil Science and Agricultural Chemistry Jawaharlal Nehru Krishi Vishwa Vidyalaya, Jabalpur 482004 (MP) ***Directorate of Extension Services Jawaharlal Nehru Krishi Vishwa Vidyalaya, Jabalpur 482004 (MP) Abstract abiotic stresses is only option to improve productivity potentials of crops. Keeping this in view farmers field trials were carried under the M.S. Swaminathan It has been observed that the productivity of cereal and pulse crops specially in black soils of Madhya Pradesh was found Research Foundation Jawaharlal Nehru Krishi Vishwa to declined continuously could be because of non adoption of Vidyalaya Collaborative project on cereal and pulse crops adequate technology by the farmers. Looking to the problem (Soybean, Wheat and Chickpea) during rabi and Kharif an experiment was frame out at farmers field to compare seasons during the year 2001 to 2005 with the following conventional system over farmers practice. In this connection objectives: i.e. front line demonstration were conducted during 2001 to 2005 at farmers field. The result data indicated that use of proper 1. To evaluate the improved land and water cultivation technologies increased yield of soybean by 53, 40, 45 & 32 %, wheat 38, 40, 46 & 39 % and chickpea 42, 43, 46 management practices for achieving high & 42 % with application of GRD + FYM in comparison to agricultural production in black soils of central India farmers practice. The data also clearly indicated that the through participatory approach and production levels was more than 50% higher when the integrated nutrients applications was used (GRD + FYM 2. To evaluate the impact of integrated nutrient treatments). In terms of improvement in quality of produce as management practices for improving productivity well as high net monitor returns. of soybean-wheat and soybean-chickpea cropping system in black soil region of Madhya Pradesh. Keywords: Front Line Demonstrations, Vertisols, Adoption of integrated nutrient management. Material and methods Black clay soils of Madhya Pradesh characterized with Front line demonstrations on selected farmers was under relatively flat to sloppy topography required proper soil taken by department of Soil Science & Agricultural and water management practices. Therefore, adoption Chemistry, Jabalpur. Soybean, Wheat and Chickpea of improved technology to conserve soil & water using crops were grown at farmers field of Dhangidhana and high yielding varieties which are resistant to biotic and Murlipodi villages of district Narsinghpur, Madhya 231 Table 1. Performance of recommended varieties of crops Crop & Season No. of FLDs Area (ha) Yield (q/ha) % Increase CD at 5% In FLD Far. Practice Soybean Kharif 2001-02 8 3.20 26.96 12.44 53 2.16 Soybean Kharif 2002-03 15 6.00 27.93 16.87 40 2.16 Soybean Kharif 2003-04 7 2.80 31.88 17.50 45 1.30 Soybean Kharif 2004-05 10 4.00 24.17 16.42 32 2.45 Cost Benefit Ratio (2001-05) 2.23:1 0.83:1 Grain Straw Ratio 1.925 2.100 Wheat Rabi 2001-02 8 3.20 30.69 19.13 38 1.10 Wheat Rabi 2002-03 15 6.00 34.73 20.86 40 0.58 Wheat Rabi 2003-04 4 1.60 35.88 19.25 46 1.25 Wheat Rabi 2004-05 11 4.40 33.73 20.55 39 1.14 Cost Benefit Ratio (2001-05) 2.64 1.15:1 Grain Straw Ratio 1.70 1.91:1 Chickpea Rabi 2001-02 8 3.20 22.21 12.79 42 0.52 Chickpea Rabi 2002-03 7 2.80 22.41 12.84 43 0.73 Chickpea Rabi 2003-04 5 2.00 22.60 12.20 46 1.45 ChickpeaRabi 2004-05 9 3.60 21.83 12.56 42 0.85 Cost Benefit Ratio (2001-05) 4.02:1 1.75:1 Grain Straw Ratio 1.959 2.151 Pradesh. These experiments were monitored regularly Chickpea crop (JG322) were selected. Integrated plant by the scientists to guide the farmers and feed back nutrient practice was used in farmers field demonstration. information were also collected for further improvement Integrated application of organic manures was applied in research and extension programs. Field days and and rest of the nutrient requirement is compensated with group meetings were also organized at the chemical fertilizers. The nutrient supplied through organic demonstration sites to provide the opportunities to other inputs was adjusted. Data were collected from the demo farmers to witnessed the benefits of the recent farmers and analysed with the suitable statistical tools technologies. The demonstrations were conducted on to compare the yield whereas each farmers treated as a area consisting of approximate one acre each. separate replication and soil samples were analysed as Recommended seed rates were used. The variety of per recommendations outlined by the Puri et al. (2000). soybean crop (JS335), wheat crop (GW273) and Results and discussion Table 2. Seed Replacement in Villages (Q) Soybean, Wheat and Chickpea Front line demonstration on kharif (soybean) Crop Seed Replacement in quintals 2001-02 2002-03 2003-04 2004-05 Field trails of Soybean crop were successfully conducted Soybean 4.0 72.00 215.40 310.25 at eight sites in 2001-02, at fifteen sites in 2002-03, seven Wheat 4.0 409.34 556.80 670.70 sites in 2003-04, ten sites in 2004-05 in Dangidhana & Gram 3.5 245.92 284.90 390.10 Murlipodi villages of Narsinghpur district during Kharif Table 3. Training, field days and farmers sangosthi during 2001 to 2005 Activity No. of Programs No. of Trainees 2001-02 2002-03 2003-04 2004-05 2001-02 2002-03 2003-04 2004-05 Training Program 2 20 6 - 125 550 179 - Farmers sangoshti 2 4 4 4 450 595 595 980 Field days 4 2 2 2 385 55 70 439 Total 8 26 12 6 950 1175 844 1419 232 Table 4. Distribution of respondents of Soybean, Wheat and Gram Crop Statement Fully adopted Partially adopted Non adopted Did you grow high yielding variety 25(100) 00(00) 00(00) Did you follow Seed treatment 12(48) 01(04) 12(48) Do you grow high yielding varieties according to their sowing time 23(92) 01(04) 01(04) Do you use bio fertilizers 13(52) 05(20) 07(28) Do you use Chemical Fertilizers N 24(96) 01(04) 00(00) P 24(96) 01(04) 00(00) K 14(96) 08(32) 03(12) Did you follow plant protection measures 09(36) 10(40) 06(24) season, respectively. The data in Table-1 revealed that respectively (Anonymous 2005). during 2001-02, eight trials of Soybean (JS335) were demonstrated. An average yields of 26.96 q/ha was Impact obtained as compare to 12.44 q/ha. During Kharif season 2002-03, fifteen trails were demonstrated with an average yield of 27.93 q/ha as compared to 16.87 q/ha. In black soils (Vertisols) of Madhya Pradesh, water Season 2003-04 and 2004-05, seven and 10 trails of stagnation during Kharif season is a common problem, Soybean (JS335) were demonstrated with an average due to low infiltration rate of the soils and fairly high rainfall of 31.88 q/ha & 24.17 q/ha as compared to 17.50 q/ha & as a result planted crops suffer for water and oxygen in 16.42 q/ha, respectively. The average cost benefit ratio the root zone. Many farmers kept the land fallow in Kharif of soybean crop was 2.23:1 as compare to farmers season (This process is called as “Haveli” Cultivation) practice 0.83:1 with grain straw ratio i.e 1.925 as and cultivate crops in post rainy season with the stored compared to farmer’s practice. The results were in soil moisture. This might be leads to loss of water and conformity with Rajput and Verma (1993) and Saxena soil due to erosion cropping system followed by farmers et al. 1990. of the region (Anonymous 2002). Land management treatments were also Front line demonstration on Rabi (wheat and chickpea) demonstrated and tested with respect to farmers practice. The treatments provided adequate drainage, The performance of front line demonstrations on cereal minimizing water lodging and erosion thus conserving rainwater in the soil, integrated plant nutrient supply and pulses during Rabi season of 2001-02, 2002-03, 2003-04 and 2004-05 exhibited the performance of crop needs to be superimposed to study the effect of improved production by innovated scientific method as compared management practice on rainfed crops. The breeder to farmers practice on wheat (GW273) and Chickpea seed of Soybean, Wheat and Gram crop was introduced in both the villages initially. As a result the FLD resulted (JG322) in Table-1. The data revealed that during Rabi 2001-02 season, eight demonstrations of wheat and in much higher productivity than farmers practice. Proper Chickpea covering 3.2 ha of land in two villages, resulted trainings of different aspect of appropriate agricultural in yield of 30.69 q/ha and 22.21 q/ha as compared to practices were given from time to time (Table-3). The 19.13 q/ha and 12.79 q/ha of farmers practice. Wheat seed of all three crops were also replaced in both villages yield on average was 34.73, 35.88 and 33.73 q/ha in over the years. Which depicted clear impact of seed replacement on yield (Kalyani 2004 and Yadav et Rabi season 2002-03, 2003-04 and 2004-05, as compared to 20.86 q/ha, 19.25 q/ha and 20.55 q/ha due al.2007). to farmers practice respectively. Seven, five and nine Adoption of improved practices demonstrations of chickpea were laid out in the year 2002-03, 2003-04 and 2004-05 covering an area of 2.8, 2.0 and 3.6 ha of land, respectively. An average yield of A report (Table 4) specify the impact of training which 22.41, 22.60 and 21.83 q/ha as compared to farmers revealed that farmers adopted the used high yielding practice i.e. 12.84, 12.20 and 12.56 q/ha, in the year varieties of Soybean, Wheat and Gram crop with 2002-03, 2003-04 and 2004-05, respectively were recommended dose of fertilizers (96%), according to recorded. The average cost benefit ratio calculated for their sowing time (92%), seed treatment (48%) and bio wheat and Chickpea crop was 2.64:1 and 4.02:1 as fertilizers (52%). Partially adopted practices by majority compare to farmers practice 1.15:1 and 1.75:1, of trainees which included use of plant protection 233 measures (40%) followed by potash application (32%), mit esa lkFkZd o`f) ns[kh x;h lefUor ikS/k iks"k.k i)fr viukus ls whereas 48% farmers did not adopt recommended practices and seed treatment, biofertilizers (28%), plant u flQZ Qly dh xq.koRrk esa lq/kkj gksrk gS ,oa izk—frd lalk/kuksa ds protection measures (24%) (Anonymous 2004). mi;ksx ls ykxr esa deh Hkh vkrh gS lkFk gh vf/kd ldy vk; Hkh izkIr gksrh gSA e/;izns’k dh dkyh e`nkvksa esa mUur rduhdh ds vHkko esa /kkU; ,oa nky Qlyksa dh mRikndrk ,oa mRiknu esa fujarj deh vk jgh gS References ijarq —"kdksa ds [ksrksa ij mUur rduhdh ds iz{ks= izn’kZu dj fdlkuksa Puri G, Gorantiwar SM and Bhargava OD (2000) Bulletin on esa rduhdh dh tkx#drk ykbZ tk ldrh gSA mijksDr igyqvksa dks / Fertilizer recommendation in Madhya Pradesh ;ku esa j[kdj ,e- ,l- LokehukFku fjlpZ QkmUMs’ku ,oa tokgjyky through soil test crop response. Department of Soil usg# —f"k fo’ofo|ky;] tcyiqj }kjk dh xbZ la;qDr ifj;kstuk esa Science & Agricultural Chemistry, J.N. Krishi Vishwa Vidyalaya, Jabalpur. ftyk ujflagiqj esa fdlkuksa ds ;gkW o"kZ 2001-02 ls 2004-05 rd Rajput AM and Verma AR (1993) Stabilization of productivity vUksdks iz{ks= izn’kZu Mkys x;s ftuds ifj.kke Lo#i ikjEifjd [ksrh through improved watershed technology in Malwa Region of Madhya Pradesh. Crop Res Journal 53, 40, 45 32 dh rqyuk esa lks;kchu Qly esa Øe’k% ,oa izfr’kr] 6(2):195-201 xsgwW esa 38, 40, 46 ,oa 39 izfr’kr rFkk pus esa 42, 43, 46 ,oa 42 Saxena KK, Jain N and Pandya SC (1990) Transfer of rainfed izfr’kr lkFkZd o`f) vkadh x;hA lkFk gh ik¸kk x¸kk fd lefUor ikS/ technology its adoption by the farmers in Malwa (Manuscriptregion. Received: Indian 12.08.2011; Res J Ext Accepted: Edu 26 20.09.2011)(3 & 4)70-73 50 k iks"k.k i)fr ds mi;ksx ls lHkh Qlyksa esa izfr’kr ls T;knk Yadav VPS, Kumar R, Deshwal AK, Raman RS, Sharma BK and Bhela SL (2007) Boosting Pulse Production Through Frontline Demonstration. Indian Res J Ext Edu 7 (2 & 3) May & September 2007 234 JNKVV Res J 45(2): 235-239 (2011) Efficient production and marketing of soybean in Central India: An initiative of ITC e-Soya Choupal H.O. Sharma and S.B. Nahatkar Department of Agricultural Economics and Farm Management Jawaharlal Nehru Krishi Vishwa Vidyalaya Jabalpur 482004 (MP) Abstract services as competitive measures with the markets established by APMCs (Birthal et al. 2007). Direct An introduction of corporate culture in the form of ITC e-soya purchase of agricultural produce from the farmers’ field Choupal creates a congenial soybean production and by individuals as well as companies, societies, co- marketing environment in Madhya Pradesh. This resulted in operatives is encouraged to reduce the number of enhanced benefits to soybean growers and developed users’ intermediaries thereby providing opportunity in increasing friendly environment in the domain. The benefited soybean the share of farmer in consumer rupee. Contract farming growers were earn more profit due to narrowed and efficient is popularized for the assured sale at the predetermined marketing channel, which reduces price spread on the one price before sowing. Specialized market yards for special hand and it directly indirectly enhance the efficiency of soybean commodities were also developed to provide a production and thus profitability on the other. These benefits are also associated with supply of quality seed and other commodity specific modern marketing infrastructure for associated inputs along with dissemination of information the particular crops grown in a specified area. Public- through electronic and other media on soybean production Private Partnership (PPP) for establishment and technology. Therefore, it is suggested that corporate management of markets for agricultural produce and to environment should also be develop for marketing of other encourage the private investment and professionalism agricultural produce through PPP mode and some of the “A” in agricultural marketing including post harvest handling grade regulated markets should be transform to corporate of agricultural produce and encouraging value addition culture for efficient marketing of agriculture inputs and outputs. to share the burden and provide healthy competition with APMC’s is encouraged in the recent past. E-market, e- Keywords: Production, Marketing, Soybean, ITC-e- soya marketing, and e-trading for speedy and distance choupal transactions were also established (Balakrishnan 2010). ITC was emerged as a new marketing channel in the The efficient agriculture marketing system is of a vital domain of agriculture marketing. The e-choupal initiative importance for development of agri-based economy. The is enabling Indian agriculture significantly enhance it’s entry of corporate sector creates healthy competitive competitiveness by empowering Indian farmers through environment which have both beneficial and detrimental the power of electronic media. effects. Some agricultural economists claimed that ITC’s Agri Business Division, one of India’s largest competition serve as a mechanism for determining the exporters of agricultural commodities, has conceived e- best suited group politically, economically and Choupal as a more efficient supply chain aimed at ecologically. On the negative side competition can have delivering value to its customers around the world on a an adverse affects on the participant groups. This may sustainable basis. The e-Choupal model has been also drain valuable resources and energy. In the present specifically designed to tackle the challenges posed by era of liberalization, the agricultural marketing is the unique features of Indian agriculture, characterized liberalized and gates are open for the alternative by fragmented farms, weak infrastructure and the marketing channels for participation in marketing process involvement of numerous intermediaries. The e-soya (Kumar et al 2005). Private companies, co-operatives Choupal’ also unshackles the potential of Indian farmers or any legal entity was establish and operate the who has been trapped in a vicious cycle of low risk taking agricultural marketing infrastructure and supporting 235 ability, low investment, low productivity, weak market This may be due to higher investment on quality orientation, low value addition, low margin and low risk production inputs. The EMC group of soybean growers taking ability. This made Indian agribusiness sector invested more on use of culture (576.77%), manure globally less competitive, despite rich and abundant (61.25%), fertilizers (100.32%), micronutrients and natural resources and mega biodiversity. Such a market- growth regulators (256.12%), weedicides (208.42%), led business model can enhance the competitiveness insecticides (114.33%) and machine power (118.37%) of Indian agriculture and trigger a virtuous cycle of higher as compared to TMC group (Table-1). On the contrary productivity, higher incomes, and enhanced capacity for they were found to invested lower on hired human labour farmer risk management, larger investments and higher (-28.74%), family labour (-40.19%) and bullock quality and productivity of the produce. How far this labour (-48.76%). Indirectly this has been substituted by particular emerging marketing channel (EMC) benefited machine power in EMC group of soybean growers. In to the farmers as compared to the traditional marketing both the groups the major share of the total cost is channel (TMC) i.e. regulated market in Madhya Pradesh accounted for use of labour and power (>45%) followed for enhancing production and marketing efficiency of by seed and seed treatment (>11%). The higher soybean is the matter of intend study. percentage expenditure on manures and fertilizers and plant protection chemicals by the soybean growers belong to EMC group reflect towards their awareness Research Methodology and access to improved production inputs. Amongst all the districts of the Madhya Pradesh, Sehore The overall cost per hectare was higher in EMC district was selected purposively due to its remarkable group as compared to TMC group. It is higher by Rs. position in soybean production and introduction of first 3176.34 per hectare (20.54%). But the enhancement in ITC e-soya choupal (2004) in the district. The Sehore productivity was only 12.12 per cent in EMC group over block of Sehore district was also selected purposively its counterpart. This revealed that the additional cost for the study as maximum area under the cultivation of incurred (20.54%) by the EMC group of soybean growers soybean was confined to this block along with presence was not transform in the output in a required rate and of ITC e-soya choupal. A list of all the villages in the this may be due to managerial constraints. The gross jurisdiction of the ITC e-soya choupal was prepared and income was higher in EMC group (Rs 4905.00 & 15.77%) 3 villages were selected on the basis of area under but income at variable and total cost was only higher by soybean. For the collection of primary data a list of all 12.12 and 11.05 per cent respectively. This also reflects soybean growers of the selected villages was prepared in input output ratio which was higher in TMC group as and 50 soybean growers associated with ITC e-soya compared to EMC group despite of higher investment choupal were selected (Group as EMC) for the study. by the soybean growers of the EMC group. This lead to An equal number of soybean growers (50) who marketed conclude that only investing more in the soybean their produce to regulated (Group as TMC) market were production without proper time and other managerial also selected for comparison between these two skills will not transform input in to the output with the marketing channels. The primary data were collected required efficiency and therefore rate of return may by the survey method and were related to agricultural decline with higher investment in soybean production. year 2009-10. Although EMC i.e. ITC e-soya choupal is supplying quality inputs to the soybean growers with required knowledge and skill of soybean production but this has Results and Discussion not been transformed in the required rate at field level. This may be due to managerial and other constraints Enhance Profitability persist amongst soybean growers in the study area. The majority of the soybean growers were reported that Efficient marketing they were using high yielding varieties, quality seeds, manures/fertilizer, insecticides and weedicides and The 95 per cent of produced soybean was disposed off engaged hired labour and machines power for cultivation only in 4 months (October-January) after the harvesting of soybean. As regards to the total cost of soybean of the product by the soybean growers of TMC group. cultivation per hectare, it was higher (Rs18640.08) in But due to specified standards / norms of grading in the EMC group as compared to TMC group (Rs15463.74). EMC, they do not purchase soybean just after harvesting 236 Table 1. Comparative cost and profitability of soybean cultivation in TMC and EMC groups (Rs./ha) Particulars Cost Cost & profit Percentage TMC EMC over TMC difference Labour & Power Hired Human labour 3673.35 2617.60 -1055.75 -28.74 Family labour 1126.48 673.77 -452.71 -40.19 Bullock Labour 518.38 265.61 -252.77 -48.76 Machine power 2190.40 4783.13 2592.73 118.37 Sub total 7508.61 8340.11 831.50 11.07 (48.59) (44.74) Seed and seed treatment Seed 2040.57 2080.35 39.78 1.95 Culture 10.89 73.70 62.81 576.77 Seed Treatment 7.62 7.73 0.11 1.44 Sub total 2059.08 2161.78 102.70 4.98 (13.31) (11.60) Manure & Fertilizers Manures 69.88 112.68 42.80 61.25 Fertilizer 400.94 803.15 402.21 100.32 Micro-nutrients & growth regulators 25.80 91.88 66.08 256.12 Sub total 496.62 1007.71 511.09 102.91 (03.21) (05.40) Protection chemicals Insecticides 161.32 345.75 184.43 114.33 Weedicides 352.61 1087.51 734.90 208.42 Sub total 513.93 1433.26 919.33 178.88 (03.32) (07.69) Indirect variable costs Depreciation 54.76 119.58 64.82 118.37 Interest on Working Capital 45.56 46.03 0.47 1.04 Sub total 100.32 165.93 65.61 65.40 (0.65) (0.89) Total variable cost 10678.56 13108.79 2430.23 22.76 (69.058) (70.32) Fixed Cost Interest on fixed capital 120.40 130.77 10.37 8.61 Rental value of owned land 4664.78 5400.52 735.74 15.77 Total Fixed Cost 4785.18 5531.29 746.11 15.59 (30.95) (29.68) Total Cost of Cultivation 15463.74 18640.08 3176.34 20.54 (100) (100) Profitability Yield (q/ha) 16.81 18.85 2.04 12.13 Gross Income 31098.50 36003.50 4905.00 15.77 Income at variable cost 20419.94 22894.71 2474.77 12.12 Input output ratio at VC 2.91 2.74 - - Income at total cost 15634.76 17363.42 1728.66 11.05 Input output ratio at TC 2.01 1.93 - - TMC: Traditional marketing channel (regulated market) EMC: Emerging marketing channel (e-choupal) (Figures in parentheses shows percentage to total cost of cultivation) 237 Table 2. Comparative marketing cost and price spread in TMC & EMC groups (Rs/q) Particulars TMC % EMC % Quantity marketed (q) 14.28 - 15.97 - Loading & Unloading Cost 3.99 13.02 0.00 0.00 Transport Cost 16.27 52.96 16.95 75.62 Storage Cost 2.95 9.60 2.36 10.63 Mandi Tax 0.09 0.30 0.00 0.00 Development Cess 1.00 3.25 0.00 0.00 Sampling of Produce 0.00 0.00 2.04 9.19 Wastage cost 5.39 17.55 0.00 0.00 Other Fees Paid 0.02 0.06 0.01 0.06 Marketing cost borne by farmers 30.73 100.00 22.21 100.00 Actual cost of the produce 1039.04 - 1011.06 - Cost of Bags 29.96 15.39 28.26 33.19 Loading & Unloading Cost 13.12 6.74 9.08 10.66 Transport Cost 59.26 30.43 30.89 36.28 Administrative Charges 7.16 3.68 12.56 14.75 Storage Cost 1.86 0.96 1.85 2.17 Mandi Tax 44.26 22.73 0.00 0.00 Development Cess 22.13 11.37 0.00 0.00 Weighing Cost 4.95 2.54 1.77 2.08 Brokege Expenses 1.03 0.53 0.00 0.00 Wastage 10.16 5.22 0.00 0.00 Other Fees Paid 0.83 0.43 0.74 0.87 Marketing Cost borne by whole Seller 194.72 100.0 85.15 100.0 Price paid to farmers 1850.0 - 1910.00 - Whole Seller’s Margin 306.89 - 246.89 - Price paid by the processor 2351.61 2242.04 Price Spread 501.61 27.11 332.04 17.38 Marketing Efficiency (ratio) 2.69 - 4.75 - TMC: Traditional marketing channel (regulated market) EMC: Emerging marketing channel (e-choupal) due to high moisture contain and therefore none of the to the cost borne by the TMC group. The major soybean grower belong to EMC group sale his produce component of marketing cost borne by the soybean during the month of October. This is one of the major producers were transportation, storage and sampling reasons for not preferring this channel (EMC) of cost in EMC group, while it was transportation, wastage, marketing by soybean growers, who want to dispose loading and unloading costs in case of TMC group. And off his produce just after harvesting for want of money. therefore actual cost of the produce was higher (Rs In TMC group 12.06 per cent of the produce was sell to 1039.04/q) in case of TMC group as compared to EMC the whole sellers just after harvesting ( at regulated group (Rs 1011.06/q). market) at lower rate with high moisture content. The marketing cost borne by the whole seller was Out of the total per hectare production of 16.81 Rs194.72 /q when the produced is sold through regulated and 18.85 in TMC and EMC groups of soybean growers market. The cost borne by the e-soy choupal kiosk was respectively, the extent of marketed surplus was 14.26q only Rs 85.15/q when the produce is sold through ITC (84.94%) and 15.97q (84.72%) per hectare. The e-soya choupal. The reasons for higher cost incurred by marketing cost borne by the soybean producers in two the whole seller operated in regulated market is that he groups was remarkably differs. It was Rs. 30.73 per q in has to bear higher transportation cost, high marketing TMC group and Rs 22.21 per q in EMC group. This taxes, development cess and wastage cost which was revealed that the marketing cost borne by the soybean not a part of marketing cost except transportation cost producers belong to EMC group was less as compared when the produce is sold through ITC e-soya choupal. 238 The price realized by the farmers belonging to EMC are either supervised or carried out by the public group was higher (Rs1910.00/q) as compared to price extension system with load of other responsibilities and (Rs 1850.00/q) realized by the soybean growers belong lack of manpower on the other hand these functions are to TMC group. The margin of ITC e-soya choupal was effectively carried out by the private extension agencies less (Rs. 246.89) as compared to the whole sellers (Rs associated with soybean industry, since processor is the 306.89) operated in the regulated market. Processor ultimate profit maker, because it is still not consume as also benefited through ease access to quality product food product in India and most of the produce is process (soybean) at remunerative prices (Rs 2242.04) from ITC for production of edible oil. Therefore, it is suggested e-soya choupal. The extent of price spread was only Rs that the other corporate sectors like ITC should also 332.04q (17.38%) when the produce is sold through ITC- come forward for providing and strengthening knowledge e-soya choupal, while it was Rs 501.61 (27.11%) when of soybean growers on full package of practice, it was disposed off through traditional marketing supported by the ease access to quality inputs at channels (TMC-Regulated market). subsidized rate and guarantee of purchased at fair price. The Government should also provide special tax relief The respondents related to the EMC group on such initiatives of the processors and corporate reported that they were sold their product to ITC e- sectors. These efforts will definitely bridge the yield gap choupal when it was convinced by the of ITC Sanchalak on the one hand and will help in enhancing production e-soya choupal, who have been facilitated with computer of soybean and hence farmers income on sustainable and internet at his home in the village. The Sanchalak of manner. e-soya choupal disseminated information on prices according to different grades before the actual marketing process. The other reasons for selecting the EMC (e- e/;izns’k esa vkbZ-Vh-lh- bZ&lks;k pkSiky ds :i esa dksjiksjsV dYpj soya choupal), as reported by the soybean growers were ds izkjaHk gksus ls lks;kchu mRiknu ,oa foi.ku dk lqn`<+ okrkoj.k superior services (100%), superior infrastructure (100%), low cost of marketing (100%), higher price/ fair price of cuk gSA ftlds QyLo:i blls lEcfU/kr {ks=ksa esa lks;kchu mRikndksa the product (100%), proximity (70.27%), no hidden dks vf/kd ykHk izkIr gqvk gS rFkk fe=or~ mi;ksxh i;kZoj.k fufeZr charges or bribe (100%) and no waiting time (100%). gqvk gSA blls fgrxzkgh lks;kchu mRiknd] tgkWa ,d vksj n{krkiw.kZ Apart from these reasons the ITC-e-soya choupal also facilitate them through supply of quality input (62.16%). foi.ku ek/;e feyus ls de ewY;&izlkj dk ykHk izkIr djrs ik;s x;s Besides this, the member also benefited through gSa ogh nwljh vksj] mUgs mUur cht] xq.koRrk;qDr vknkuksa dh iwfrZ purchase of quality consumer goods from ITC Sagar rFkk lks;kchu rduhd ds leqfpr izlkj dk ykHk izkIr gksus ls izR;{k Mall at reasonable prices. The majority of respondents related to the EMC reported that the facilities of storage, rFkk vizR;{k :i ls muds lks;kchu mRiknu esa n{krkiw.kZ o`f) ikbZ auction arrangement, sale supervision, loading, parking x;h gSA vr,o] ;g lq>ko fn;k tk ldrk gS fd vU; —f"k mRiknksa , telephone/ internet were more efficient. Where as, such dks Hkh yksd&futh Hkkxhnkjh ds ek/;e ls dksjiksjsV dYpj ls tksM+k facilities are not so effectively available to the soybean tkos rFkk dqN *v* Jzs.kh dh —f"k mit ef.M;ksa dks blds vuq:i — growers who sale their produce through TMC. f"k vknkuksa ,oa mRiknksa ds n{krkiw.kZ foi.ku ds fy, ifjofrZr fd;k tkosA Conclusion It is clear from the above discussion that ITC e-soya References choupal is found to be technically viable, economical feasible and efficient channel for soybean marketing in Balakrishanan R (2010) Corporate Entry into Agricultural input the central part of India. Therefore, the efforts should and Output Markets and Its Impact on Small be made to facilitate the producers, traders and regulated Producers and Consumers. Indian J Agric Eco 65 markets in the lines of the ITC e-choupal. Further, it is (1): 40-47 also observed during the course of investigation that the Birthal Pratap S, Jha Awadesh K, Singh Harvinder (2007) Linking Farmers to Markets for High-value farmers who were related to the TMC were lacking in Agricultural Commodities. Agric Econ Res Rev 20: the technical knowledge, supply of quality inputs and 425-439 (Manuscript Received: 20.08.2010; Accepted: 30.05.2011) ease access to information technology. These functions Kumar K N, Ravi Lakshmi K Sree, Rao B Bapuji (2005) Agricultural Marketing in India in the era of Globalization- Constraints and Suggestions. Agric Marke 48 (3): 30-37 239 JNKVV Res J 45(2): 240-243 (2011) Web based information system of aromatic plants using IT tools for Agri-Rural development A.K. Rai, Bharati Dass, Ratnesh Sahu and Akhilesh Raghuwanshi Instrument Development and Service Centre Jawaharlal Nehru Krishi Vishwa Vidyalaya Jabalpur 482 004 (MP) Abstract and to some extent at globe level. Aromatic plants are grown in forest whereas some species can also be grown India is having a vast treasure of aromatic plants grown in the at farmer’s field by cultivation. Cultivation in central India forest areas as well as field crops. Presently the knowledge is possible because wide variation in soil and climate in of aromatic plants grown at different level is limited to botanist, the region. horticulturist, and industries engaged in the production. The knowledge of aromatic plants naturally grown in forest is also Madhya Pradesh possesses a vast treasure of known to agri-rural & tribal as they use these products in the diverse aromatic plants, which occur as flora in natural treatments of some diseases. This knowledge is slowly forests distributed throughout the state Agrawal et al disappearing due to non availability of information at larger (2004). Some forest areas considered rich in natural level. Now a day, there are many knowledge centers of genetic resources have been identified as Hot Spots of aromatic plants by way of books, websites and annual reports aromatic plants. The tremendous amount of diversity and of different organization working in this field. However, floristic pattern is due to variations in climatic, edaphic comprehensive database of each plant variety, its location, and topographic features of the State. Madhya Pradesh identification, productivity, marketing and industrial uses needed to be augmented. Almost all aromatic plants can be is natural habitat for over 50% of the herbs used in the cultivated by the farmers provided knowledge of latest package industry and is key source of raw material to the domestic of practices can be made readily available to them. The data herbal industry. The farmers and extension agencies are base contain the information of importance of each aromatic need to be trained on scientific way of cultivation and species, their visualization in industry, productivity figures and processing of aromatic plants. Cultivation and processing shortage if any along with export potential of each species. It of aromatic plants are very labour intensive and have also highlight the recent technological development for the also tremendous potential for employment generation cultivation of aromatic plants as field crops. The most important and poverty alleviation at rural level. More than one aspect of the information to be collected and to be updated million people are engaged in gathering, processing, on day to day basis regarding availability and requirement aspects of these products in different market. Keeping this in manufacturing and marketing in the country. There is a view, authors have developed a web application which is useful growing demand both in India and abroad. Therefore, to farmers, growers, agri-rural and tribal’s, planners, industries the demand for raw material will also increase etc. The web application is bilingual i.e. in Hindi and English correspondingly. One of the ways to increase productivity & developed using PHP & MYSQL for aromatic plants grown is to adapt modern agronomical techniques in growing in central India. aromatic plant at farmer’s fields. Even though, the land mass of India occupies only Keywords: Aromatic Plants, Data Base, Treasure, 2 % of the globe, it occupies 11% of the total known Therapatic, PHP, MYSQL world flora and one of the world’s top 12 mega diversify nations. India having different soil types with varied Aromatic plants are the source for the manufacturing of climate conditions is ideally suited for the cultivation of perfumes, flavors and cosmetics in the world [Farooqie these plants. The cost of production is considerably low and Sreeramu (2004). India possesses rich and diverse since many of these can be grown and harvested round source of aromatic plants for fulfilling the demand of the year. It provides steady employment to agricultural cosmetic industry based on these plants at national level labour and income to farmers. Most of the aromatic 240 Fig 1: Flow Diagram Showing Aromatic Plants Species 241 plants can be grown as intercrops in the farmers’ fields. The proposed database will be comprehensive. The agriculture extension workers and advanced farmers Presently the knowledge of aromatic plants grown will be able to use the database for finding answers to at different level is limited to botanist, horticulturist, and their problems and also for growing better varieties of some industries engaged in the production. The aromatic plants having very good marketing potential knowledge of aromatic plants naturally grown in forest (Dwivedi et al. 2006). The database is bilingual i.e. in is also known to agri-rural & tribal as they use these Hindi and English. Multimedia tools, audio video clips, products in the treatments of some diseases. This sounds and text are supporting each module. The knowledge is slowly disappearing due to non availability system developed in such a way that the user can interact of information at larger level. Now a day, there are many with the database for obtaining information for a set of knowledge centers of aromatic plants by way of books, aromatic plants. websites and annual reports of different organization working in this field. However, comprehensive database The title clearly indicates that the database will covering most of the aspects of aromatic plants has not dynamically help in educating agriculturist, horticulturist, been prepared. A comprehensive database of each plant extension workers and others engaged in the field of variety, its location, identification, productivity, marketing aromatic plants. It consist of information in modular and industrial uses needed to be augmented. Agro fashion, so that the user can choose a particular module climatic condition of central India is suitable to grow depending upon requirement. It is interactive in such a aromatic plants (www.dmap.org.in). Almost all aromatic way that simple scavy knowledge of computer operation plants can be cultivated by the farmers provided will be enough to browse different modules. The primary knowledge of latest package of practices can be made aim is to utilize the IT tools for effective dissipation of readily available to them. available technological inputs through GUI. The database address mainly to different type of users. The first one is The internet is to facilitate the dissipation of for importing knowledge for the cultivation of aromatic comprehensive knowledge of aromatic plants to growers plants at the farmer’s field. This modules have sub and different level of user group. This also includes modules and each sub module consist of sub-sub amateur who want to start small scale ventures based module depending upon major activities perform for on Aromatic plants. The knowledge will be key based cultivation of particular plant species. All type of variations and bilingual in Hindi and English on web portal with in technologies due to agro climatic regions is also digital photo gallery. incorporated wherever needed. The most suitable aromatic plants will be suggested. If someone is already Methods growing aromatic plants then the operator will have an option for the recent package of practices for improving the quality and obtaining optimum yield. For the creation The web based data base generation, application module of above module and other sub modules huge database covering different aspects of crop production (with a is created by gathering information on each species in separate sub-module) designed and developed using terms of their genetic variability, cultivation practices, PHP (for dynamic web application). The database of farm seed availability, harvest and post harvest technologies, management technologies can be stored and updated chemical composition of these species for their use in using MYSQL. These tools are completely free ware. therapies (Bhattachayi 2008; Sharma 2003). The In the recent year interest in the aromatic plants database will be also incorporates taxonomical feature has increase to an extent. Throughout the globe scientist of the each species, their scientific nomenclature and are busy in finding the aromatic values of aromatic plants local nomenclature in Hindi and English (Gangrade et (www.ayurvedic_medicines.com). The demands for al. 2003; Singh and Somadey 2005). The marketing aromatic plants are increasing day by day. But for information is also incorporated. So that maximum profit reference presently a complete website regarding can be achieved by the cultivator (Anon 2008). The cost cultivation practices of aromatic plants is no where of cultivation in brief is incorporated. The flow diagram available on the internet. Development of database of of the developed database is shown in Fig 1. Detailed aromatic plants on internet will fulfill the requirement in list of aromatic plants is given in Table1 which includes all the aspects of availability of knowledge of the stated trees, shrubs, herbs and climbers. field. This field has generated the demand for plant and their products in the country. Efforts are being made to Expected Results introduce many of these aromatic plants into the Indian agriculture (www.nmpb.nic.in). 242 The system may work as an assist to farmers and fodflr fd;k x;k gS A experts in furnishing knowledge on various aspects of aromatic plants. The aromatic plants sector suffers from lack of availability of good quality planting materials, lack References of uses of hybrid seeds and poor farm management. Hence the production technology needs improvement Farooqi Azhar Ali, Sreeramu BS (2004) Cultivation of Medicinal qualitatively and quantitatively, so that the standards of and Aromatic Crops. Universities Press (I) Pvt Ltd the aromatic plants after processing produce can be Hyderabad. further improved to cater the acceptability of international Agrawal VK, Rawat Anay, Udahayaya SD (2006-07) market. At present whatever production technology is Aushadhiya Awam Sagandh Poudh Ki Kheti, available that is through extension bulletins published Jawaharlal Nehru Krishi Vishwa Vidyalya, Jabalpur by different extension agencies or through aromatic MP. plants seed production farms. In the software the recent www.dmap.org.in technologies available in the country is collected and www.ayurvedic-medicines.com incorporated in different modules so that user can easily www.nmpb.nic.in access all the modules or module of his/her interest. Dwivedi SK, Agarwal VK, Tiwari AB, Anay Rawat, Upadhyaya Information technology will play a pivotal role in SD (2006) A Resource Book on Prioritize Aromatic & agriculture extension activities and the database of above Aromatic Plants of Madhya Pradesh, Jawaharlal nature will definitely help the extension workers as most Nehru Krishi Vishwa Vidyalya Jabalpur MP. of the village panchyat may have personal computer in Bhattacharjee Supriya Kumar (2000) Handbook of Aromatic near future. It can be easily converted in any other Indian Plants, Indian Agriculture Research Institute, New languages as per need and requirement. Delhi Sharma Ravindra (2003) Aromatic Plants of India-An Encyclopadia, Green Foundation, Dehradun, Hkkjr o"kZ esa lax/k ikS/kksa dh cgqrk;r laink gS] tks fd ouksa esa ik;h Uttarnchal tkrh gS] rFkk [ksrksa esa Hkh mxk;h tkrh gS A vkt ds le; esa lxa/ Gangrade Surendra Kumar, Tripathi Nitin Kumar, Harinkhede Dilendra Kumar & Mishra Pramod Kumar (2003) k ikS/kksa dh [ksrh dh tkudkjh dsoy ouLifr;ksa] m|ku fo'ks"kKksa ,oa Ethno-Aromatic Diversity Used By Tribal’s of Central m|ksxksa tks budk mRiknu djrs gSa mUgsa gh fo'ks"k tkudkjh gS A ouksa India, Jawaharlal Nehru Krishi Vishwa Vidyalya Jabalpur MP esa izk—frd :i ls ikbZ tkus okyh lxa/k ikS/kksa dh tkudkjh ogk¡ ij Singh MP, Somadey (2005) Indian Aromatic Plants, Birsa jgus okys xzkeh.kksa ,oa tutkfr;ksa dks gh irk gS D;ksafd os budk Agricultural University, Ranchi, Jharkhand mi;ksx jksxksa ds mipkj ds fy, djrs gS A ;g Kku /khjs&/khjs yqIr Anon (2008) Monthly Statistic (2006-07) of the foreign trade of India. gksrk tk jgk gS A D;ksafd lxa/k ikS/kksa ds mRiknu fof/k cM+s Lrj ij miyC/k ugha gS A dqN tkudjh fdrkcksa] csclkbVksa ,ao fofHkUu (Manuscript Received: 29.05.2011; Accepted: 30.06.2011) laLFkkvksa dh okf"kZd fjiksVksZ esa gS A izR;sd ikS/kksa dh fofHkUu fdLesa] igpku] mRiknu] cktkj Hkko] vkS/kksfxd mi;ksx bR;kfn dh O;kid tkudkjh ds fy, ,d MsVkcsl cukus dh fQj Hkh vko';drk gS A mijksŸk fopkjksa dks /;kku esa j[krs gq;s ys[kdksa us ,d ,slk osc vk/ kkfjr lwpuk ra= dk fodkl fd;k gS ftlesa lax/kh; ikS/kksa dh mUur [ksrh dh lEiw.kZ tkudkjh miyC/k gS A bl osc vk/kkfjr lwpuk ra= dks fgUnh ,oa vxzsath nksuksa Hkk"kkvksa esa fodflr fd;k x;k gS A bl lk¶Vos;j dks ih-,p-ih- ,oa ek; ,l-D;w-,y- dk mi;ksx djrs gq;s 243 JNKVV Res J 45(2): 244-252 (2011) Accessing electronic resources at University Central Library in the digital eon: an appraisal Siddarth Nayak, Vinita Pandey, Sharad Kumar Jain and Preeti Sagar Nayak Jawaharlal Nehru Krishi Vishwa Vidyalaya Jabalpur 482004 (MP) Abstract The article describes the availability of information resources use resources intensively in own homes or work places amidst digital eon. Electronic access of library usage tend to (Kumar 2009). Library is a place where knowledge and bring the library in the forefront. This paper assists faculty, information is available in various forms. Today more staff and students by providing information on suitable emphasis is laid on soft information or more precisely accessible resources for reading, learning, researching, on digitized form of information (Pandey et al. 2009). referencing and for information gathering from available library collections in print and those available in digital format. The searching of any required literature must be highly targeted, focused and rapid so also the result obtained should be authentic, accurate and viable to be feasible Keywords: Central library, digital, information, data to support findings. documentation, information, access, journal An agricultural library primarily renders service to the policy makers, specialists, scientists, teachers, Physical space to house an ever growing collection has students, researchers and farmers in agriculture and always been a problem for the library. As the prevalence allied subject (Visakhi 2009). Central library is constantly and prominence of electronic resources continues to striving for adoption of novel and innovative technologies grow in academic libraries, it is not surprising that there so that all the users are benefitted from library services. is a large and increasing body of research in this area Although huge volumes of printed holding are available (Gottfried 2011). The scope of research, extension, in the library as stored data but still there is an enormous teaching, education and training cannot be confined, want of timely information which is available on the learning essentially begins at the level of an individual internet unexplored due to lack of information. and library is a place where learning is developed. Life is always lived forward but understood backwards, for faculty, staff and students’ successful lifelong learning Central Library is a way of life and it’s their inherent ability to effectively and efficiently utilize literature available in the library. Also The Central Library of Jawaharlal Nehru Krishi Vishwa science does not recognize geographical boundaries Vidyalaya (JNKVV) was established in the year 1964 (Florence 2008) and for this reason the users must Central Library is currently linked with 60 national always be aware of global online resources available on institutes and Universities under exchange programme, the web. The coming age of the electronic journals has VSAT connectivity is available for internet browsing at altered the way scholarly information is disseminated ARIS lab (Pandey et al. 2009). Recently with NKN throughout the world (Gleeson 2001). They have not only (National Knowledge Network) support high speed affected the way information is spread, but the way (10Gbps) connectivity is also made available. The information is acquired and how scientific researchers Central library of Jawaharlal Nehru Krishi Vishwa seek that needed information (Guruprasad et al. 2009). Vidyalaya is well equipped with core i3 and Pentium IV Libraries have been significantly transformed with computers supported by data capturing unit, bar code the advent of Internet and the ability to provide resources scanners, bar code printer, thermal power printer, flat to people who may never visit a physical building, but bed scanner, laser printer and photocopier. Twelve computers are installed in the library for consultation. 244 Information Sources publications, patents, books, etc (http://en.wikipedia.org/ wiki/Bibliographic_database). Library is places where different sources of information Below are the brief descriptions of available consortiums, are available to the users. Although information sources abstracting services. bibliographic databases: are classified as primary, secondary and tertiary, but they are also classified on account of the information availability in digital format. Three forms of electronic e-TOC access are currently available are i.e. full text access, abstract access and bibliographic databases. Apart from e-TOC stands for electronic table of contents, it is of this the information on net is available to the library users utmost importance as it acts as a catalyst to bring the in the form of consortium, abstracting services and users to the library to access the full contents of the bibliographic databases. desired literature. The faculty, staff and students first normally register for e-TOC services and then the table Consortiums of contents of required periodical is sent to them via email as soon as its new issue arrives in the library or is released by the publisher. E-TOC acts as an alert service A consortia is an association of two or more individuals, for the user so that information is collected within the companies, organizations or governments (or any speculated time combination of these entities) with the objective of participating in a common activity or pooling the resources for achieving a common goal. Consortium is CeRA a Latin word, meaning ‘partnership, association or society’ and derives from consors ‘partner’, itself from CeRA stands for Consortium for e-Resources in con- ‘together’ and sors ‘fate’, meaning owner of means Agriculture. CeRA was launched successfully on 30 April or comrade. Library consortia, providing for physical and 2008 at its headquarters at Indian Agricultural research electronic delivery of materials, and integrating the Institute (IARI), New Delhi, India. The published materials collection-development process are all distinct and form publishers of Agriculture, Veterinary Science, crucial steps in moving toward the twenty-first century Fisheries, Crop Science, Computer Science, Soil library. Consortia are tools, which aid in exploiting the Science, Animal Science fields are available for full as features of the e-resources in effecting savings (Singh well as for partial access. At present the journals from and Rao 2008). the domain Elsevier, Springer Link, Annual Reviews, Indian Journals.com, Taylor and Francis, Oxford Abstracting services Journals, Wiley Online Library, CSIRO (Australia) and American Society of Agronomy are available to the users. In all, a total of 2747 journals are available for access. An abstract is an abbreviated summary of a research The CeRA facility is functional at the computer lab of article, review, or any in-depth analysis of a particular Central Library and can be accessed by faculty, staff subject or discipline, and is often used to help the reader research scholars and students (http://www.cera.jccc.in/ quickly ascertain the paper’s purpose. When used, an ). abstract always appears at the beginning of a manuscript, acting as the point-of-entry for any given scientific paper or patent application (http:// JCCC www.websters-online-dictionary.org). Abstracting services normally constitute a database of available JCCC stands for J-Gate Custom Content for Consortia, journal articles. Abstracting services helps us in locating i.e. the gateway for accessing journals is one of the a particular article in the ocean of published literature. A services provided by M/s Informatics India Limited, citation is always available with an abstracting service. Bangalore. They are providing two types of services under J-Gate viz. J-Gate Custom Content i.e. JCC (for Bibliographic databases - A bibliographic individual libraries, a service not subscribed) and J-Gate database is a database of bibliographic records, or an Custom Content for Consortia i.e. JCCC (for consortium organized digital collection of references to published of libraries), CSIR Consortium through its Indian National literature, including journal and newspaper articles, Science & Technology Information Resources Consortia conference proceedings, reports, government and legal (INSTIRC) has collaborated with Informatics India to offer 245 JCCC to all the CSIR laboratories and the same is known UGC-DAE Consortium for Scientific Research as JCCC-INSTIRC (http://www.instirc.jccc.in) UGC-DAE Consortium for Scientific Research, DAE INDEST Library Consortia, Department of Atomic Energy. It was established to promote interaction amongst the scientists working in the research centres of the Department of This consortium is named as the Indian National Digital Atomic Energy and the faculty from the universities and Library in Science and Technology (INDEST) Consortium. other institutions of higher learning and also acting by The Ministry of Human Resource Development (MHRD) providing specialized training and advanced has set-up a consortia-based Subscription to Electronic characterization facilities to University researchers; Resources for Technical Education System in India. The making facilities of DAE accessible to university consortium is named as the Indian National Digital Library researchers and to add a new concept of researchers in in Science and Technology (INDEST) Consortium. E- academic institutions working together. (http:// INDEST Consortium is the most ambitious initiative taken www.csr.res.in/) so far in the country The Ministry of Human Resource Development (MHRD) has set-up a Consortia-based Subscription to Electronic Resources for Technical IIM’s Library Consortia Education System in India (http://indest.iiita.ac.in/ and http://paniit.iitd.ac.in/indest/) This consortium is active at Indian Institute of Management (IIM) Library Consortium. The Indian UGC-Infonet Digital Library Consortium Institute of Management (IIM) Library consortia is a e- content digital network system available at all the IIM over India. The resources related to IIM’s curricula are The Consortium provides current as well as archival available in this consortium (http://www.iimahd.ernet.in/ access to more than 7000+ core and peer-reviewed library/vslib/consortia.htm). journals and 10 bibliographic databases from 26 publishers and aggregators in different disciplines. The programme is wholly funded by the UGC and executed DOAJ by the INFLIBNET (Information and Library Network) Centre, Ahmedabad (http://www.inflibnet.ac.in/econ/ DOAJ stands for the Directory of Open Access Journals. index.php). DOAJ essentially lists those journals which are available in open access to its access-or. It provides us with free, MCIT Libraries Consortium full text, quality controlled scientific and scholarly journals, covering all subjects and many languages and currently about 6568 journals are available in DOAJ out of which It is a Resource Sharing and Networking of Libraries. 2918 journals searchable at article level and contain Ministry of Communications and Information Technology, 574391 articles. DOAJ is accessible. Currently DOAJ Government of India, comprises of three departments has 318 journals India. The agriculture section is having those are Department of Information Technology (DIT); 340 open access from journals (http://www.doaj.org). Department of Telecommunication (DOT) and; Department of Post (DOP). Each department has a number of PSU/Organizations/Autonomous Bodies. Open J-Gate These organizations have their own Libraries, Documentation Centres and Information Centres to meet Open J-Gate is an electronic gateway to global journal the information needs of their officials. Sometimes it literature in open access domain. It was launched in the has been observed that these libraries purchase year 2006. Open J-Gate is the contribution of Informatics common/similar information resources separately. Thus (India) Ltd to promote open access initiative (OAI). Open there is a need of common purchasing and sharing of J-Gate provides seamless access to millions of journal information resources among these organizations for the articles available online. Open J-Gate is also a database best utilization of their library budget as well as of journal literature, indexed from 8701 open access information resources. MCIT Libraries Consortium has journals, with links to full text at publisher sites (http:// the above areas in its purview (http:// www.openj-gate.com/). mcitconsortium.nic.in/). 246 World Bank eLibrary Project Muse is a unique collaboration between libraries and publishers, providing 100% full-text, affordable and user-friendly online access to a comprehensive selection The World Bank eLibrary is the World Bank’s of prestigious humanities and social sciences journals. subscription-based collection of nearly 6,000 books, Heavily indexed and peer-reviewed journals are available reports, journals, and working papers. eLibrary contains for access. Currently, Muse provides full-text access to only the final, peer-reviewed versions of the bank’s formal current content from over 482 titles representing nearly publications. eLibrary is designed to meet the needs of 100 not-for-profit publishers. Project Muse’s mission is the library, academic and research community. Its value to excel in the broad dissemination of high-quality lies in the quality and convenience of its publications scholarly content. Through innovation and collaborative database. Each article, book, and working paper is development, Project MUSE anticipates the needs of enriched with semantic and bibliographic metadata, and delivers essential resources to all members of the including topics, regions, countries, abstracts and scholarly community (http://www.muse.jhu.edu/journals/ keywords using a controlled vocabulary (http:// ) elibrary.worldbank.org). CSIRO publishing Cambridge University Press CSIRO publishing is Australia´s Commonwealth The Cambridge University Press journals can be Scientific and Industrial Research Organization. CSIRO accessed at http://journals.cambridge.org/. The publishing operates as an independent science and Cambridge University Press is currently publishing about technology publisher with a global reputation for quality 250 peer-reviewed academic journals which are easily products and services. It is internationally recognized accessed online. The dedication of Cambridge University publishing programme covers a wide range of scientific Press is to advancing knowledge. Apart from all other disciplines, including agriculture, the plant and animal subject field the press publishes journals from sciences, and environmental management. CSIRO Agriculture, Science & Technology and physical sciences publishing publishes journals, books and magazines. The (http://journals.cambridge.org). contents CSIRO publishing are available as print and online. (http://www.publish.csiro.au) Emerald Portland press Emerald is a leading independent publisher of global research with impact in business, management society, Portland press is a leading provider of high quality public policy and education. Emerald publishes about publishing and knowledge dissemination solutions. The 200 journals. Emerald is fully searchable and available available journals can be accessed at http:// online, the user can view the information needed www.portlandpress.com/pp/. Portland press is a not-for- whenever and wherever (http:// profit publisher of various journals and books. About 12 www.emeraldinsight.com/). peer reviewed journals in the field of biosciences, biochemistry and cell biology are published by Portland Project Euclid press currently (http://www.portlandpress.com). Mathematics and statistics are integral part of agricultural Oxford University Press sciences. All research work is incomplete without mathematics and statistical analysis. Project Euclid has Oxford Journals are published by Oxford University full-text searching, reference linking, interoperability Press, Currently about 230 academic and research through the Open Archives Initiative (OAI), and long-term journals are published covering a broad range of subject retention of data are all important components of the areas, two-thirds of which are published in collaboration Project Euclid. About 63 important journals are published with learned societies and other international in under this project (http://www.projecteuclid.org/). organizations. The mission of Oxford Journals (Oxford University Press) is to bring the highest quality research Project Muse to the widest possible audience (http:// www.oxfordjournals.org/). 247 J-STOR high-quality content (books, journals, newsletters, CD- ROMs, online platforms, protocols, databases and conferences). Springer has some 2,000 journals and JSTOR is a not–for–profit service that helps scholars, more than 7,000 new book titles every year. The main researchers, and students discover, use, and build upon publishing fields are science, technology, medicine, a wide range of content in a trusted digital archive of business, transport and architecture (http:// over one thousand academic journals and other scholarly www.springer.com). content. Currently JSTOR is regarded one of the world’s most trusted sources for academic content (http:// www.jstor.org/). Bentham Science Publishers Medknow Bentham Science Publishers is a major STM journal publisher of 106 online and print journals and related print/online book series, Bentham Science answers the Medknow publications is a publisher for peer-reviewed, informational needs of the pharmaceutical, biomedical online and print+online journals in the area of science, and medical research community. Bentham open publish technology and medicine. Medknow is the largest open over 230 peer-reviewed open access journals. These access publisher publishing on behalf of learned free-to-view online journals cover all major disciplines societies and associations. It pioneers and follows in ‘fee- of science, technology, medicine and social sciences less-free’ model of open access publishing and provides (http://www.benthamscience.com/). immediate free access to the electronic editions of the journals. It has more than 135 print+online journals. More than 50,000 manuscripts are available on the Medknow EBSCOhost website database (http://www.medknow.com/). EBSCOhost, the most-used premium research service Taylor & Francis in libraries and other institutions worldwide, EBSCO offers a suite of more than 300 full-texts and secondary research databases covering all subject areas, levels of The Taylor & Francis Group publishes more than 1,500 research, and user communities EBSCOhost Electronic journals and around 1,800 new books each year, with a Journals Service (EJS) is the gateway to thousands of books backlist in excess of 20,000 specialist titles. The e-journals containing millions of articles from hundreds Taylor & Francis Group are providers of quality of different publishers (http://www.ebscohost.com/) information and knowledge that enable users to perform their jobs efficiently, continue their education, and help contribute to the advancement of their chosen markets. National Agricultural Library The users of Taylor & Francis Group are researchers, students, academics and increasingly professionals. The US Department of Agriculture’s National Agricultural Taylor & Francis Group is an Informa business (http:// Library (NAL) is the world’s largest and most accessible www.informa.com). Informa plc is the global information research library focused on agriculture. NAL serves the provider for the academic, professional and commercial world with information germane to agriculture and its markets around the whole world. (http:// related sciences. Many of the Library’s services and www.taylorandfrancisgroup.com/). information resources are available to customers worldwide via the NAL website. NAL collections contain Springer nearly 4 million items. The Library subscribes to more than 16,000 current periodicals, and has access to nearly 4,500 journals and newspapers in electronic formats, Springer is one of the topmost publishers and provide as well as about 50 other specialized electronic throughout the world scientific and professional resources. These NAL resources provide access to communities with superior specialist information. billions of pages of information and data covering all Springer has an history of 165 years publishing. Springer aspects of agriculture and its related sciences, dating a leading global scientific publisher, delivering quality from the 16th century to the present, an immense array content through innovative information products and of scientific literature, books and journals, audiovisuals, services. Springer eBook Collection has more than reports, theses, software, laser discs, artifacts, and 45,000 titles. The product range across all media for images in agriculture are available (http:// 248 www.nal.usda.gov). apart from its competitors by emphasizing high standards of quality in content and presentation in all of its products. (http://www.thieme.com/) NOPR NOPR stands for, NISCAIR online periodicals repository, Compendex NISCAIR (National Institute of Science Communication and Information Resources) hosts online its research Compendex is one of the most comprehensive journals in NOPR. In all 17 research journals are engineering literature databases. With over 13 million available under open access mode for accessing the records across 190 engineering disciplines, Compendex full text. Research communities including students not delivers the comprehensive, precise information and only in India but all over the world are being benefited by insights that researchers need. Compendex is available open access of NISCAIR journals. It helps in enhancing on Engineering Village, users get results that are the accessibility and visibility base of NISCAIR journals consistently accurate. (http://www.ei.org/compendex) at National and International level. Current issues of all the research journals are published in NOPR for open access well before the publication of print version. The Elsevier BIOBASE repository has data spanning from 2007 till current issues and for some journals from 2002 onwards. At present Elsevier BIOBASE is a bibliographic current awareness the repository has about 6400 articles (http:// database providing comprehensive coverage of the nopr.niscair.res.in) entire spectrum of biological research worldwide. Elsevier BIOBASE enables pure and applied scientists Wiley Online Library to keep up-to-date with literature published in their subject area by providing a subject categorized listing of titles, authors, abstracts, bibliographic details and authors’ Wiley Online Library hosts the world’s broadest and addresses. Elsevier BIOBASE has a unique, deepest multidisciplinary collection of online resources comprehensive classification scheme. Titles are covering life, health and physical sciences, social presented under appropriate subject headings, to provide science, and the humanities. It delivers seamless information which is accurate and relevant to the integrated access to over 4 million articles from 1500 respective person’s area of activity/research journals, almost 10,000 online books, and hundreds of (www.elsevier.com/wps/product/cws_home/600715). reference works, laboratory protocols and databases. The main feature are a clean and simple interface, this online service delivers intuitive navigation, enhanced Embase discoverability, expanded functionalities and a range of personalization and alerting options (http:// Embase is the most comprehensive online source of www.wiley.com/). biomedical answers. With over 20 million records from more than 7,000 active authoritative journals, with Thieme Embase your research needs are fully met. Holding over 1,800 biomedical titles not offered by Medline, Embase delivers comprehensive, authoritative, reliable coverage Thieme is an award-winning international medical and of the most relevant biomedical literature. Embase is science publisher serving health professionals and supported by unique life science thesaurus Emtree. students for more than 125 years. Thieme promotes the Comprehensive and polyhierarchically structured, latest advancements in clinical practice, publishes the Emtree provides a consistent description of all latest research findings, advocates medical education biomedical terminology, allowing unrivaled drug and and is known for the high quality and didactic nature of disease indexing and retrieval (http://www.embase.com). its books, journals, and electronic products. In addition to publishing 70 new book titles every year, Thieme publishes more than 130 medical and scientific journals Ei EnCompass both in traditional print and electronic format, a number of which are printed on behalf of professional societies, The Ei EnCompass Databases aim to meet the as well as dozens of online products. Thieme still stands information needs of industries by providing timely, 249 comprehensive, and reliable access to worldwide related Internet resources (http://agris.fao.org/ and http:/ technical literature, patents, and business and economic /en.wikipedia.org/wiki/AGRIS). views. With access to over 1.5 million selections, the EnCompass database is the premier international ScienceDirect information source for the petroleum, petrochemical, downstream, natural gas and energy industries. Providing timely as well as technical literature dating from ScienceDirect is one of the leading full-text scientific 1942 and patents from 1963, Ei EnCompass provides database offering journal articles and book chapters from historical technical literature, patent information, more than 2,500 peer-reviewed journals and more than technology applications, and business news and market 11,000 books. There are currently more than 9.5 million intelligence. All information is easily found because of articles/chapters, a content base that is growing at a precision searching based on a controlled vocabulary. rate of almost 0.5 million additions per year. The platform (http://www.ei.org/encompasslit_pat) offers sophisticated search and retrieval functionality that enables the user to maximize the effectiveness of their knowledge discovery process. New tools available in the AGRICOLA website facilitate research work flow aids such as access to content at an early publication stage and efficient AGRICOLA (AGRICultural OnLine Access) is a database multiple document downloading of content that can be created and maintained by the United States Department stored, printed and passed to colleagues(http:// of Agriculture. The database serves as the catalog and www.sciencedirect.com/). index for the collections of the United States National Agricultural Library, but it also provides public access to Web of Science information on agriculture and allied fields. AGRICOLA indexes a wide variety of publications covering agriculture and its allied fields, including, animal and veterinary Web of Science ® provides researchers, administrators, sciences, entomology, plant sciences, forestry, faculty, and students with quick, powerful access to the aquaculture and fisheries, farming and farming systems, world’s leading citation databases. Authoritative, agricultural economics, extension and education, food multidisciplinary content covers over 10,000 of the and human nutrition, and earth and environmental highest impact journals worldwide, including Open sciences. Tens of thousands of AGRICOLA records Access journals and over 110,000 conference contain links to full-text resources on the web proceedings. You’ll find current and retrospective encompassing all aspects of agriculture and allied coverage in the sciences, social sciences, arts, and disciplines and covering materials in all formats. (http:// humanities, with coverage available to 1900. Overcome en.wikipedia.org/wiki/AGRICOLA and (http:// information overload and focus on essential data across www.agricola.nal.usda.gov/) 256 disciplines (http://www.isiwebofknowledge.com) AGRIS PMN Plant Science Database It is International System for Agricultural Science and The PMN Plant Science Database accesses thousands Technology. AGRIS is a global public domain database of indexed, web-based resources, including all articles with 2644818 structured bibliographical records on published in PMN journals, as well as fact sheets, agricultural science and technology. 82.09% of records newsletters, product listings, training materials, and other are citations from scientific journals. This database is resources hosted at PMN’s university, industry, and non- maintained by FAO, and its content is provided by more profit partner sites. PMN’s basic Public Search function than 150 participating institutions from 65 countries. is available to all users. The Public Search locates all AGRIS allows scientists, researchers and students to items in the Plant Science Database but does not allow perform sophisticated searches using keywords from the for refined searches. (http:// AGROVOC thesaurus, specific journal titles or names www.plantmanagementnetwork.org/). of countries, institutions, and authors. The bibliographic references contain either links to the full text of the SciFinder publication or additional information retrieved from 250 SciFinder is a research discovery tool that allows college lsok,sa vfxze iafDr esa vk xbZ gSa A ;g 'kks/ki= f’k{kd] f’k{kkfonksa] students and faculty to access a wide diversity of oSKkfudksa ,oa Nk=ksa dks bUVjusV ij miyC/k fofHkUu 'kks/k izdk’ku research from many scientific disciplines, including biomedical sciences, chemistry, engineering, materials laLFkkvksa@laxBuksa ds ckjs esa laf{kIr fooj.k ds lkFk mudh tkudkjh science, agricultural science, and more. With a single miyC/k djkrk gS] ftlls tkudkjh;ksa ds lzksrksa ,oa muds ,d=hdj.k source, you can explore scientific information in journal esa enn feyrh gS A and patent literature from around the world, as well as reputable web sources References from more than 10,000 currently published journals and patents from References more than 61 patent authorities’ important scientific discoveries from the present to the mid-1800s. (https:// Florence Alexander T (2008) The role of national science scifinder.cas.org/.) journals: information, advocacy and education Commentary. Thai J Pharmaceutical Sci 1-3 Gleeson Amy C (2001) Information seeking behaviour of Conclusion scientists and their adaptation to electronic journals. Masters paper for the MS in Library Science degree, To support experimental research findings relevant School of Information and Library Science University of North Carolina, Chapel Hill reference literature is required and to obtain such suitable previously published results and findings we should be Gottfried John C (2011) Access to Business Research Resources Through Academic Library Websites: A very particular towards what is to be searched, how its Survey. J Business & F Librarianship 16 : (1) 1-32 is to be searched and from where it is to be searched. Guruprasad R Nikam Khaiser Rao M Gopinath Mudkavi The searches should be aimed for obtaining quick results Vidyadhar (2009) E-Journal Usage and Scholarly that are genuine, precise, correct and feasible to support Communication using Transaction Log Analysis: A experimental research findings. Weakness should be Case Study of E- Journal (Full Text) Download recognized by us and we should always be ready to learn Patterns of NAL Scientists and Engineers. 7th from bodies performing better than us. We should be International CALIBER-2009, Pondicherry University engrossing new technologies at all levels and should be Puducherry February 25-27 2009 550 - 564 ready to adopt innovations for creating sustained http:// www.doaj.org/ differentiation in the learned society. http://agricola.nal.usda.gov/ http://agris.fao.org/ http://elibrary.worldbank.org/ Acknowledgements http://en.wikipedia.org/wiki/AGRIS/ http://en.wikipedia.org/wiki/Bibliographic_database/ The electronic information resources mentioned in the http://indest.iiita.ac.in/ and http://paniit.iitd.ac.in/indest/ manuscript have been collected and compiled by the http://isiwebofknowledge.com/ authors to make the staff and students aware of the http://journals.cambridge.org/ available resources. The authors thank the respective http://mcitconsortium.nic.in/ websites and their owners from where the content has http://nopr.niscair.res.in/ been drawn for non commercial and nonprofit use. The http://scifinder.cas.org/ authors also thank Indian Council of Agricultural http://www.benthamscience.com/ Research (ICAR), National Information Centre (NIC) for http://www.cera.jccc.in/ giving VSAT connectivity and BSNL-(National http://www.csr.res.in/ Knowledge Network) NKN service for uninterrupted and timely access of websites of publishing houses. http://www.ebscohost.com/ http://www.ei.org/compendex/ http://www.ei.org/encompasslit_pat/ ;g 'kks/ki= —f"k vuqla/kku ,oa vUos'k.k ls lacaf/kr lwpuk,Wa ,oa www.elsevier.com/wps/product/cws_home/600715/ tkudkfj;ksa ds bysDVªkWfud miyC/krk ds ckjs esa gS A iqLrdky;ksa esa http://www.embase.com/ bUVjusV ds vkxeu ls iqLrdky; mi;ksxdrkZ dks lalk/ku miyC/k http://www.emeraldinsight.com/ djkus dh {kerk esa vlk/kj.k fodkl gqvk gS A orZeku le; esa http://www.iimahd.ernet.in/library/vslib/consortia.htm/ http://www.inflibnet.ac.in/econ/index.php/ lax.d@ifjdyd izkS|ksfxdh vkSj bysDVªkWfud fodkl ds dkj.k iqLrdky; http://www.informa.com/ 251 http://www.instirc.jccc.in/ Singh Kunwar Rao V Bhaskar (2008) An Overview of the http://www.jstor.org/ Library Consortia in India PLANNER – 2008. 6th http://www.medknow.com/ Convention PLANNER - 2008, Nagaland University Nagaland, November 06-07, 2008 © INFLIBNET http://www.muse.jhu.edu/journals/ Centre Ahmedabad http://www.nal.usda.gov/ Visakhi P (2009) Consortium for e-Resources in Agriculture. http://www.openj-gate.com/ DESIDOC J Library & Infor Technol, 29 (5) : 24-30 http://www.oxfordjournals.org/ http://www.plantmanagementnetwork.org/search/ http://www.portlandpress.com/ http://www.projecteuclid.org./ (Manuscript Received: 27.05.2011; Accepted: 30.08.2011) http://www.publish.csiro.au/ http://www.sciencedirect.com/ http://www.springer.com/ http://www.taylorandfrancisgroup.com/ http://www.thieme.com/ http://www.websters-online-dictionary.org/ http://www.wiley.com/ Kumar Manish (2009) Academic Libraries in Electronic Environment: Paradigm Shift ICAL 2009 – Vision and Roles of the Future Academic Libraries 105-108 Pandey Vinita Nayak Siddarth Jain Sharad Kumar and Nayak Preeti Sagar (2009) Pilot study on online and offline e- resource usage at Central Library, JNKVV, Jabalpur, Madhya Pradesh. JNKVV R Jr. 43 (1) : 117-122 252 JNKVV Res J 45(2): 253-263 (2011) Authorship distinctiveness of research papers dealing with agriculture and allied subjects published in JNKVV Research Journal Siddarth Nayak, Vinita Pandey, Preeti Sagar Nayak and Sharad Kumar Jain Jawaharlal Nehru Krishi Vishwa Vidyalaya Jabalpur 482004 (MP) Abstract interested governments, universities and funding bodies as a measure of accountability and quality sought (Steele et al. 2006). Publication is at the heart of academia, In the academic world publication of manuscripts in scholarly journals is a matter of repute, reward and also a prepare for working in whatever field we do, researching without getting recognition, gaining reputation, career advancement publishing our findings or even our perspectives and and for promotional hierarchy. A total of 51 papers including thoughts on our discipline is against the public interest, 7 short communications were published in the year 2009 in there is quite properly an increasing pressure on JNKVV Research Journal. JNKVV Research Journal volume academics in all parts of the world to publish (Florence 43, number 1 and 2 contributed towards the result of this study. 2008). Scientific productivity is generally measured in An analysis of authorship characteristics like gender, status terms of the published output. One of the most affiliation, rank etc and authorship trend in the present journal fundamental norms of science for scientists is to were analyzed. The results indicate male dominance in promulgate their research findings among their peers publication of research papers. The tendency of multiple authorship is on the rise and a combination of three authors (Gupta et al. 1999). Also according to Karisiddappa et published maximum papers. Authors of Jabalpur published al. (2002) the scientific productivity of authors in the maximum number of research papers. Madhya Pradesh was context of R&D is normally measured in terms of their the most productive state in terms of paper productivity. The published scientific output (research papers, reports, degree of collaboration worked out to be 0.960. Maximum books and monographs) and technical output (patents, numbers of authors were from Department of Plant Breeding processes, innovations, etc.). and Genetics One way of determining how knowledge is transferred and used in science is by studying the Keywords: author, authorship, JNKVV research journal, literature published in the discipline, especially articles research published in journals (Sanni and Zainab 2010). Scientists are not only expected to develop knowledge; they are The authorship of any scientific publication is based on also required to share the knowledge they develop with personal effort of author/authors and their research, other members of peer communities through formal innovativeness, creativity and originality. Readers often sources in order to be considered to have contributed to look for authenticity, novelty, knowledge, accuracy and knowledge (Mengxiong 1993). Work is valuable, recentness, whereas journal paper, authorship because it provides an opportunity to understand the comprises for author’s responsibility and accountability breadth and depth of inquiry in our discipline (Brunn of manuscript. Scientific journals are the means by which 1992). Also the evaluation of the scientific performance the scientific community certifies accumulations and of individual researchers is mainly based on the analysis additions to its body of accepted knowledge and the of the publication record (Schoonbaert and Roelants means through which scholars are competing (Hargens 1996). According to Loannidis (2008) the future of single 1988 and Merton 1957). Measurement of research scientists, teams and large institutions increasingly excellence and quality is an issue that has increasingly depends on ‘‘publish-or-perish’’ (or ‘‘get-cited-or-perish’’) principles. 253 JNKVV research journal is a multidisciplinary agricultural journal publishing in the field of agriculture, forestry, agricultural engineering, veterinary science and animal husbandry. It is an official publication of Jawaharlal Nehru Krishi Vishwa Vidyalaya, Jabalpur, Madhya Pradesh (MP) India. The first issue of the journal was published in the year 1967. At that time the journal was published as quarterly. At present the journal is published biannually, i.e two issues per year. Currently the journal is being abstracted in CAB international abstracting system, Biological Abstracts and Indian Science Abstracts. reveals that out of the total of 158 authors 26 (16.455%) Material and methods were female and 132 (83.544%) were male. It is of clear evidence that male authors far outnumber the female Papers required for the present study have been taken authors; this may be attributed to the fact that number of from the published issue of JNKVV research journal, female researchers is less as compared to male volume 43, number 1 and 2, year 2009. The study is researchers. There is a growing concern in the most conducted on 51 research papers. The main authorship advanced countries about women and science issues characteristics of authors like name, gender, affiliation, and, more specifically, about the under-representation designation hierarchy etc were obtained and then of women in scientific careers (Mauleón and Bordons compiled and analyzed for generating result. The degree 2006). of collaboration was worked out using the formula given by Subramanyam (1983). Table 1. Gender of authors Author Gender Number Percentage (%) Dc = Nm ÷ ( Nm + Ns) Female 26 16.455 Where N = number of multi-authored papers and Male 132 83.544 m Total 158 100 Ns = number of single authored papers The number of male authors far outnumbers female Result and Discussion authors also according to European Commission (2006) it was found that women constituted only just over one The findings of the present work are elaborated here third of the researchers in academic medicine, agricultural sciences, social sciences and humanities Author’s gender Authorship trend The main problem bibliometric studies have to face is how to identify male and female authors, since sex is Literature on any subject reflects not only basic publishing not included in bibliographic databases. (Mauleón and pattern but also the characteristics of the authors Bordons 2006). Identification was also difficult since in themselves (Kannappanavar and Vijaykumar 2001). India gender is normally identified by first name and not Ownership and authorship determine how intentionality by surname and in most of the research papers though and self knowledge are ascribed, these ascriptions, in surname is present but first name is usually absent or turn, have implications for individual and societal attitudes only alphabets are designated in place of full names. towards those subjects whose behaviour cannot be Since the Journal is published from, Jawaharlal Nehru described in intentional terms (Bortolotti and Broome, Krishi Vishwa Vidyalaya, Jabalpur MP, identification of 2009). Table 2 indicates visibly that three authors claim gender was done on personal basis. maximum percentage (22) (43.137%) of paper followed by four authored paper 13 (25.490%). Two authored Each paper is considered as an individual papers (11) also claimed 21.56% authorship. Single separate publication and the data presented in table 1 authored, five authored and six authored papers gained 254 and inter-scientist collaboration have become essential Figure 2. Authorship trend for the advancement of science, scientists with different 25 skills and specialization profiles may successfully collaborate for the development of research projects and 20 the creation of new knowledge (Costas and Bordons 15 No. of Authors 2011). Number of authors is used as a straight forward 10 Number of papers variable to reflect co-authorship (Sutter and Kocher No of Papers of No 5 2004). 0 1 2 3 4 5 6 Table 3. Co-authorship trend No of Authors No. of Number of Cumulative Percentage Coauthors Coauthors total (%) the last scores of 2 (3.922%), 2 (3.922%) and 1 (1.961%) respectively. From the above result it is clear that three, One 11 11 22.448 four and two authored form the most preferred type of Two 22 33 44.897 authorship group, which also indicates teamwork. On Three 13 46 26.53 seeing the data it becomes well established fact that Four 2 48 4.081 multi authored papers (96.079%) far outnumber the Five 1 49 2.040 single authored papers (3.922%). Total 49 49 100 Research is rarely done in isolation, especially research Table 2. Authorship trend of an experimental rather than a theoretical bent (Fox No. of Authors Number of Cumulative Percentage papers total (%) Figure 4. Per capita authorship 0.324 Sole/One 2 2 3.922 0.323 Two 11 13 21.569 0.322 0.321 Three 22 35 43.137 0.32 Per capita authorship Four 13 48 25.490 0.319 0.318 Five 2 50 3.922 0.317 0.316 Per Capita Authorship Six 1 51 1.961 0.315 Total 51 51 100.00 Full papers Short Total communications Papers Co-authorship 1991. The current trend as depicted in table 3 shows that taking coauthors in a paper is increasing one Multi-authored documents are now the norm in science coauthor 11 (22.448%) per paper to two coauthors 22 as a result of the important role of collaboration in (44.897%) and three coauthors 13 (26.53%). Taking two coauthors was the most preferred group. Taking four or Figure 3. Coautjorship patter more coauthors showed a decreasing trend as far as 25 JNKVV Research Journal is concerned. From the data 20 it can be enlightened that small group of upto four authors are able to generate publishable research findings, which 15 No. of Coauthors show that there is an inclination towards collaborative Number of papers 10 research. No of Papers of No 5 0 Per capita authorship 1 2 3 4 5 No of Coauthors It was found that a total of 51 research papers were research (Bordons and Go´mez 2000; Hara et al. 2003). published by 158 authors during the publication year Due to the increasing complexity of research, teamwork 2009. 255 Table 4. Per capita authorship scholars in their attempt to coordinate skills, tools, and rewards” (Patel 1972).The data presented in the table 5 Type No. of Number Per capita paper of authors authorship clearly shows very high degree of collaboration in the research papers published. The degree of collaboration Full papers 44 136 0.323 was more (1.00) in short communications as compared Short communications 7 22 0.318 to full papers (0.956). If total publication were compared Total 51 158 0.322 then the degree of collaboration was 0.960 (Fig 5). The per capita authorship is calculated as: Table 5. Degree of collaboration Per capita authorship = Number of papers ÷ number of Type Degree of collaboration authors Full Paper 0.956 Short communications 1.00 The data pertaining to per capita authorship is presented Total 0.960 in table 4 and graphically represented through figure 4. The overall per capita authorship works out to 0.322. Table 6 signify that degree of collaboration among two, The per capita authorship for full research papers is three, four, five and six authors was 0.215, 0.431, 0.254, 0.323 and in case of short communications the per capita 0.039, and 0.019 respectively which infers that as the authorship is calculated to be 0.318. The results indicate number of author’s increases degree of collaboration that there is more collaborative and multidisciplinary decreases. research trend existence. Degree of Collaboration Table 6.Degree of collaboration of multi-authored papers DC DC DC DC DC In the issue under evaluation only two type of papers (two-A) (three-A) (four-A) (five-A) (six-A) were published i.e. either full papers or short communication. Scientific collaboration can be defined 0.215 0.431 0.254 0.039 0.019 as “ a process of functional interdependence between DC= Degree of collaboration, A= Author Figure 5 Degree of collaboration Status of Author 1.01 1 0.99 0.98 Figure 7. Status of authors 0.97 Degree of collaboration 0.96 140 0.95 120 Degree of Collaboration Degree 0.94 0.93 100 Full Paper Short Total (combined) communications 80 Female Papers Male 60 No of Authors 40 Figure 6 Degree of collaboration of multi-authored papers 20 0.5 0 0.45 Senior Middle Junior Total 0.4 0.35 Level of Authors 0.3 0.25 Series1 0.2 0.15 The status of the authors was analyzed; three categories 0.1 Degree of of Degree collaboration 0.05 were formed regarding the authorship status senior, 0 middle and junior. The data presented in table 7 clearly Two Authors Three Authors Fours Authors Five Authors Six Authors Authors shows that no female author exists in the senior category and only 3 female authors were present in the middle 256 category, followed by 23 authors in the junior category. identified in the title out of which 160 words were used On the other hand in total there were 132 male authors as singly, 25 words were used two times, 5 words were out of which the status of 16 authors could not be used three times, 2 words were used four times, 4 words determined. Out of 116 authors 49 authors were of senior were used 5 times, another 4 words were used 6 times category 29 were of middle category and 38 were from and 1 words was used for 7 and 9 times respectively. junior level. Although the words used singly and in double are not mentioned here due to paucity of space, but the ones From the table it can be inferred that no female which are repeated more are mentioned here, The words authors were from senior level but the trend is increasing Madhya Pradesh (MP) and the words chickpea was as more females at junior level are publishing their repeated 9 and 7 times respectively in the title followed research findings in journal, in contrast we see that more by the title words nutrient, production, quality and yield males are available as senior authors as compared to which were repeated 6 times each. The words crop, middle and junior level. India, rice and soybean were repeated 5 times each whereas the words Jabalpur and seed were repeated 4 times each. The title words genotypes, irrigation, Table 7. Status of Author productivity, rainfed and wheat were repeated 3 times each. Status Senior Middle Junior Total Total Percentage Analysis of title (%) Table 8. No. of No of times Total Percentage Female 0 3 23 26 18.309 words used in title (%) Male 49 29 38 116 81.690 Total 49 32 61 142 100 1 9 9 3.072 1 7 7 2.389 2 4 8 2.730 Analysis of Title 4 5 20 6.826 4 6 24 8.191 5 3 15 5.119 Every research paper has a suitable title provided by its 25 2 50 17.065 authors. A title is a short summary that represents 160 1 160 54.608 documents main theme, a title is a compact Total 293 100 representation of a document which contains documents main theme, so that the reader can quickly identify the information that is of interest to them (Reddy et al. 2011). The data clearly indicates that more work was done on chickpea, soybean, rice and wheat crops since The data related to analysis of title words is these title words are repeatedly used in the title of presented in Table 8, shows that there are words in title research paper. Madhya Pradesh is also known as which are repeated and used in many different titles in soybean state, ranks in pulse production and rice wheat JNKVV Research Journal; the frequency of use is given is the predominant cropping system of the state. The in the table. In all agriculturally important 293 words were journal is being published under JNKVV so most of the work is based in Madhya Pradesh (9) and it was the most used title word. Figure 8 Analysis of keywords 150 140 130 120 110 100 90 No. of key words 80 70 No of times used as key 60 words 50 40 30 No of times used as keywords as used times No of 20 10 0 1 2 3 No of Keywords 257 Analysis of keywords Every published research finding is accumulated in certain volume of printed pages. Every journal publishes papers and the numbers of published papers indicate Keyword analysis is a bibliometric approach which is the mindset of authors who confine their research used widely for profiling a field (Chiu and Ho 2007; findings to certain number of paper, the table 9 indicates McGrath 1996). It can also be used for predicting the the liking of authors towards publishing number of pages. trend of a field (Li et al. 2009; Yi and Xi 2008). As per The data clearly indicates that four printed pages had data depicted in table 8 a total of 164 keywords were 16 (31.372%) papers followed by three pages, 11 available to the users, in the two issues/number of (21.568%) papers which were the most preferred page JNKVV Research Journal, issue/number 1 had 90 key choices, while few authors went to publish six pages 8 words whereas issue/number 2 had 74 keywords. Seven (15.68%) papers followed by two 5 (9.803%) papers and short communications did not have any key words. Also five pages 5 (9.803%) papers. There were only four two full length papers did not provided their articles with papers having seven pages (7.843%) and two papers keyword. Keywords were also not present in short having eight pages (3.921%). It can be inferred that communication. It is very clear from the findings that the authors generally tend to confine their research findings one keyword soybean was repeated 4 times whereas in between 3 to 4 printed pages. twelve keywords were repeated 2 times each remaining 136 key words were used only once in keyword compilation. On analyzing the keywords crop-wise we Table 9. Publication page preference see that papers on only 16 crops (Berseem, Chickpea, No. of Number of Cumulative total Percentage Garlic, Karonda, Lemon Grass, Lentil, Mango, Niger, pages papers total (%) Oat, Pigeon pea, Potato, Rice, Sesame, Sorghum, Soybean and wheat) including agriculture, horticulture 2 5 5 9.803 and medicinal and aromatic plants were published. 3 11 16 21.568 4 16 32 31.372 Maximum work according of keywords (keyword 5 5 37 9.803 combination) was done in wheat crop (5) followed by 6 8 45 15.68 soybean (4) and rice (2). Keywords are an essential part 7 4 49 7.843 of research paper; it provides us with an overview we 8 2 51 3.921 expect that might be available in the research paper. Total 51 51 100.00 Publication page preference Number of References Figure 9 Publication pages preference Figure 10 Number of References per paper 18 30 16 25 14 12 20 No. of Reference 10 No. of Pages 15 Number of papers 8 Number of papers 10 6 No of References of No No of Papers of No 4 5 2 0 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 1 2 3 4 5 6 7 No of Papers No of Pages Table 10 summarizes the number of references cited in Table 8. Analysis of keywords each paper, on evaluation it was found that 7 was the No. of No of times Total Percentage most cited reference number about 9 papers (17.647%) key words used as key words have quoted it followed by 3 reference which has been 1 4 4 2.439 quoted in 6 papers (11.765) followed by 10 and 5 2 12 24 14.634 references each quoted by 4 papers (7.843%). Highest 1 136 136 82.926 number of references i.e 26 and were cited by 1 review Total 164 100 paper and least number of references quoted was 2 258 which was cited in 2 papers (3.922%). Departmental affiliation of author Annual assessment of scientists, academicians and Table 10. Number of references researchers is always done on the basis their contribution No. of Number of Cumulative Percentage to knowledge judged by their volume of publication in reference papers total (%) journals. As stated earlier JNKVV Research Journal is an open for all multidisciplinary agricultural research 2 2 2 3.922 journal. All individuals serving agriculture sector either 3 6 8 11.765 4 1 9 1.961 directly or indirectly with laudable finding can publish his/ 5 4 13 7.843 her work in JNKVV Research Journal. Table 12, Fig 12 6 3 16 5.882 indicates the affiliation of authors with respect to their 7 9 25 17.647 field/department of work. 8 1 26 1.961 9 3 29 5.882 In all a total of thirty seven author’s affiliations were 10 4 33 7.843 found published in the journal, the highest number of 11 3 36 5.882 authors 19 (12.025%) were affiliated to Plant Breeding 12 1 37 1.961 13 2 39 3.922 Figure 11 Single authored references vs multi authored references 14 1 40 1.961 15 1 41 1.961 400 16 2 43 3.922 350 300 17 2 45 3.922 250 Single authored references 18 1 46 1.961 200 Multi authored references 19 2 48 3.922 150 100 20 1 49 1.961 References of No. 50 21 1 50 1.961 0 26 1 51 1.961 Volume 43 Volume 43 Total number 1 number 2 Total 51 51 100.000 JNKVV Research Journal Cross reference analysis (single authored references vs multi authored references) and Genetics, followed by Extension Education JNKVV 18 (11.392%), Crop and Herbal Physiology 12 (7.595), A total of 261 references were present in volume 43 Seed Technology Research Unit Rahuri 11 (6.962%) number 1 and 222 references were present in number and Food Science and Technology 10 (6.329%). Soil 2. Overall in both the issues/number a total of 483 Science and Agricultural Chemistry of JNKVV and references were present. It is evident from Table 11, Fig. Botany of Rahuri both had 8 (5.063%) authors affiliation 11 that multi authored references surpassed single followed by Agronomy 7 (4.430%) and AICRP sesame authored references, only 118 (2.443%) references were and Niger 6 (3.797%). The departments, Agricultural single as compared to multi authored references which Chemistry and Soil Science Agra, Agricultural were 364 (75.556%). This shows that tendency of Economics and Farm Management, BTC JNKVV, authors towards multi authorship in rising. Horticulture , Allahabad, Horticulture Pantnagar, KVK Jabalpur, KVK Rewa, Plant Pathology, Seed Pathology Laboratory Rahuri each had 3 (1.899%) authors affiliation Table 11. Single authored references vs multi authored references Figure 12 Authorship affiliation Issue Single Percent Multi Percent Total 20 authored age (%) authored age (%) number of 18 references references references 16 14 12 Vol. 43 No.1 65 24.904 196 75.095 261 10 No of Authors 8 Vo. 43 No.1 53 23.873 169 76.126 222 6 Total 118 2.443 365 75.556 483 Noof Authors 4 2 0 Library JNKVV Central Plant Seed Herbal NRCS and Seed KVK Crop Crop and AICRP Extension Sorghum, Pathology Pathology Education Livestock Chemistry Rewa KVK Allahabad Production Agricultural Agricultural Economics Horticulture Technology ENGG jnkvv ENGG Sciences Soil scienceSoil sesame andsesame Chhindawara Botany Rahuri Botany Environmental Affiliating department/centre 259 Table 12. Affiliation of authors S.No. Affiliation No of Authors Cumulative Percentage 1. Agricultural Chemistry and Soil Science, Agra 3 3 1.899 2. Agricultural Economics, AAU, Anand 2 5 1.266 3. Agricultural Economics and Farm management 3 8 1.899 4. Agronomy 7 15 4.430 5. AICRP Sesame and Niger 6 21 3.797 6. Biochemistry 1 22 0.633 7. Botany Rahuri 8 30 5.063 8. BTC, JNKVV 3 33 1.899 9. Central Library 2 35 1.266 10. Chotu Ram PG College, Meerut 1 36 0.633 11. Crop and Herbal Physiology 12 48 7.595 12. DES, JNKVV 1 49 0.633 13. Engineering, JNKVV 1 50 0.633 14. Entomology 2 52 1.266 15. Environmental Sciences Magadh University, Patna 2 54 1.266 16. Extension Education, AAU, Gujarat 1 55 0.633 17. Extension Education JNKVV 18 73 11.392 18. Food Science and Technology 10 83 6.329 19. Horticulture, Allahabad 3 86 1.899 20. Horticulture, Pantnagar 3 89 1.899 21. JNKVV 7 96 4.430 22. JNKVV, Tikamgarh 1 97 0.633 23. KVK, Chhindwara 1 98 0.633 24. KVK, Jabalpur 3 101 1.899 25. KVK, Rewa 3 104 1.899 26. Livestock Farm, JNKVV 1 105 0.633 27. Livestock Production, AAU, Gujarat 1 106 0.633 28. MGCGVV, Chitrakoot 2 108 1.266 29. Sorghum, Plant Breeding and Genetics (NRCS) 1 109 0.633 30. Plant Breeding and Genetics 19 128 12.025 31. Plant Pathology 3 131 1.899 32. Post Harvest Process and Food Engineering 1 132 0.633 33. Seed Pathology Laboratory, Rahuri 3 135 1.899 34. Seed Research and Technology Centre, Hyderebad 2 137 1.266 35. Seed Technology Centre, Faizabad 2 139 1.266 36. Seed Technology Research Unit, Rahuri 11 150 6.962 37. Soil Science and Agricultural Chemistry 8 158 5.063 Total 158 158 100 (AAU – Anand Agricultural University, BTC Biotechnology Centre, JNKVV, Jawaharlal Nehru Krishi Vishwa Vidyalaya, DES Directorate of Extension Services, KVK Krishi Vikas Kendra, MGCGVV Mahatma Gandhi Chitrakoot Gramodaya Vishwa Vidyalaya, NRCS National Research Centre for Sorghum) followed by departments Agricultural Economics AAU only 1 (0.633%) author affiliation. The high percentage Anand, Central Library, Entomology, Environmental of authors from Plant Breeding and Genetics shows the Sciences Magadh University Patna, MGCGVV dominance of the department in publishing their research Chitrakoot, Seed Research and Technology Centre findings. Hyderabad, Seed Technology Centre Faizabad having 2 (1.266%) affiliations each. The departments City affiliation of authors Biochemistry, Chotu Ram PG College Meerut, DES JNKVV, Engineering JNKVV, Extension Education AAU Gujarat, JNKVV Tikamgarh, KVK Chhindawara, The data presented in the table 13 reveals that maximum Livestock farm JNKVV, Livestock Production AAU number of authors were from Jabalpur 33 (56.897%) Gujarat, NRCS Sorghum, Plant Breeding and Genetics, followed by Rahuri 7(12.069%). Other city affiliation of Post Harvest Process and Food Engineering each had authors was 2 (3.448%) each from Anand, Chitrakoot, 260 Hyderabad and Tikamgarh. The number of papers was choice. The information presented portrays how JNKVV more from Jabalpur because Jabalpur is the headquarter Research Journal is influencing authors from all over of JNKVV and is having all facilities and also the journal the country. The data presented in table 15 clearly shows is published from Jabalpur. the dominance of authors from Madhya Pradesh 42 (72.414%) followed by Maharashtra 7 (12.069%) and Uttar Pradesh 4 (6.897) and than by Andhra Pradesh City affiliation of authors Table 13. and Gujarat with 2 (3.448) each. City Occurrence Cumulative Percentage (%) Table 15. State affiliation of authors Agra 1 1 1.724 Allahabad 1 2 1.724 State Occurrence Total Percentage Anand 2 4 3.448 (%) Chhindwara 1 5 1.724 Andhra Pradesh 2 2 3.448 Chitrakoot 2 7 3.448 Bihar 1 3 1.724 Faizabad 1 8 1.724 Gujarat 2 5 3.448 Hyderabad 2 10 3.448 Madhya Pradesh 42 47 72.414 Indore 2 12 3.448 Maharashtra 7 54 12.069 Jabalpur 33 45 56.897 Uttar Pradesh 4 58 6.897 Meerut 1 46 1.724 Total 58 100.00 Pantnagar 1 47 1.724 Patna 1 48 1.724 Rahuri 7 55 12.069 Ranking of Authors Rewa 1 56 1.724 Tikamgarh 2 58 3.448 Total 58 58 100 Publication record of a scientist is representative of his/ her scientific performance (Kostoff, 1998). The ranking of the authors according to JNKVV Research Journal State affiliation of authors 2009 is presented in table 16. It is very clear that 1 author contributed 5 papers followed by another author who JNKVV Research Journal is an official and prestigious published 4 papers there were two authors who publication of Jawaharlal Nehru Krishi Vishwa Vidalaya, published 3 papers each. Followed by 28 authors who Jabalpur. Madhya Pradesh, India but it is open for all are having 2 papers each to their credit. Lastly there and any one devoted to research can publish the papers were 87 authors whose name has occurred only once if it is found suitable by editor and reviewers. Our country i.e. having 1 paper to their credit either as author or as is having many States and almost all States are having coauthor. Dr. V.K. Mandhare has published 5 papers an Agriculture University and most of them are publishing and holds the first rank followed by A.V. Suryawanshi their own journals. Still with the development of whose name has occurred in 4 papers, followed by Dr. information and communication technologies and library K.K. Saxena and Dr. S.B. Gawade whose name has exchange programme researchers get information about occurred in 3 papers. journals and opt to publish their research according to Figure 14 Dominance of author from cities Figure 15 occurrence of State 45 35 40 30 35 25 30 Occurrence 20 25 Occurrence 15 20 Occurence Occurence 10 15 5 10 0 5 0 Agra Indore Patna Rewa Bihar Anand Meerut Rahuri Gujarat Faizabad Jabalpur Allahabad Chitrakoot Hyderabad Pantnagar Tikamgarh Chhindwara Maharashtra Uttar Pradesh Andhra Pradesh Madhya Pradesh City State 261 Table 16. Ranking of Authors No of Authors Appearance of Name in Papers 1 5 1 4 2 3 28 2 87 1 Findings From the data presented the following inference is drawn • The overall per capita authorship works out to 0.322. • Majority of authors were male which shows predominance of male authors. • The ranking of authors revealed that Dr. V.K. • The highest number of authors 19 (12.025%) were Mandhare published the maximum number of affiliated to Plant Breeding and Genetics papers in the single volume and occupied the first • For JNKVV Research Journal degree of place with 5 publications. collaboration was 0.960, which is very high and clearly indicates dominance upon sole authorship. Conclusion • Analysis of authorship trend reveals that three authors claim maximum percentage (43.137%) of The current study portraits many important issues related paper (22) published. JNKVV Research Journal which were not touched • Taking two coauthors was the most preferred group before. The study reflects the authorship choices and i.e. taking per paper to two coauthors 22 (44.897%). provides a platform for effective and efficient dissemination of researcher’s findings. Advancement in • Multi authored references surpassed single science and technology is dependent upon the scientific authored references; only 118 (24.481%) work done and is brought in front of the public through references were single as compared to multi publications. The prevailing trend shows that contribution authored references which were 364 (75.518%). to agricultural research is growing and more researchers • Four printed pages 16 (31.372%) was the most are moving into it. Assessment has a key role if taken preferred page number for getting the results positively for further improvement and excellence of published. current journal. Efforts are needed to improve the way • 7 was the most cited reference number and 9 we promote and adopt research and research findings. papers (17.647%) have quoted 7 references each. • A total of 293 title words were identified and analysis References of title word shows that 1 word was common to 9 research papers. Bordons M, Go´mez I (2000) Collaboration networks in • 293 title words were identified and the title word Science. In B. Cronin & H. B. Atkins (Eds.), The web Madhya Pradesh (MP) and the word chickpea was of knowledge: a Festschrift in honour of Eugene repeated 9 and 7 times respectively. Garfield (pp. 197–213). 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